Application Requirements Research Articles

Asymmetric Charge Distribution in Atomically Precise Metal Nanoclusters for Boosted CO2 Reduction Catalysis.

Recently, atomically precise metal nanoclusters (NCs) have been widely applied in CO2 reduction reaction (CO2RR), achieving exciting activity and selectivity and revealing structure-performance correlation. However, at present, the efficiency of CO2RR is still unsatisfactory and cannot meet the requirements of practical applications. One of the main reasons is the difficulty in CO2 activation due to the chemical inertness of CO2. Constructing symmetry-breaking active sites is regarded as an effective strategy to promote CO2 activation by modulating electronic and geometric structure of CO2 molecule. In addition, in the subsequent CO2RR process, asymmetric charge distributed sites can break the charge balance in adjacent adsorbed C1 intermediates and suppress electrostatic repulsion between dipoles, benefiting for C-C coupling to generate C2+ products. Although compared to single atoms, metal nanoparticles, and inorganic materials the research on the construction of asymmetric catalytic sites in metal NCs is in a newly-developing stage, the precision, adjustability and diversity of metal NCs structure provide many possibilities to build asymmetric sites. This review summarizes several strategies of construction asymmetric charge distribution in metal NCs for boosting CO2RR, concludes the mechanism investigation paradigm of NCs-based catalysts, and proposes the challenges and opportunities of NCs catalysis.

A Single-Cell Interrogation System from Scratch: Microfluidics and Deep Learning.

Live-cell imaging using fluorescence microscopy enables researchers to study cellular processes in unprecedented detail. These techniques are becoming increasingly popular among microbiologists. The emergence of microfluidics and deep learning has significantly increased the amount of quantitative data that can be extracted from such experiments. However, these techniques require highly specialized expertise and equipment, making them inaccessible to many biologists. Here we present a guide for microbiologists, with a basic understanding of microfluidics, to construct a custom-made live-cell interrogation system that is capable of recording and analyzing thousands of bacterial cell-cycles per experiment. The requirements for different microbiological applications are varied, and experiments often demand a high level of versatility and custom-designed capabilities. This work is intended as a guide for the design and engineering of microfluidic master molds and how to build polydimethylsiloxane chips. Furthermore, we show how state-of-the-art deep-learning techniques can be used to design image processing algorithms that allow for the rapid extraction of highly quantitative information from large populations of individual bacterial cells.

Graphfingerprint: graph embedding of graphs with almost constant sub-structures

AbstractIn some machine learning applications, graphs tend to be composed of a large number of tiny almost constant sub-structures. The current embedding methods are not prepared for this type of graphs and thus, their representational power tends to be very low. Our aim is to define a new graph embedding that considers this specific type of graphs. We present GraphFingerprint, which is a new embedding method that specifically considers the fact that graphs are composed of millions of almost constant sub-structures. The three-dimensional characterisation of a chemical metal-oxide nanocompound easily fits in these types of graphs, which nodes are atoms and edges are their bonds. Our graph embedding method has been used to predict the toxicity of these nanocompounds, achieving a high accuracy compared to other embedding methods. The representational power of the current embedding methods do not properly satisfy the requirements of some machine learning applications based on graphs, for this reason, a new embedding method has been defined and heuristically demonstrated that achieves good accuracy.

Effect of N,N′-Methylenebisacrylamide on properties of porous semi-IPN hydrogels from Acrylamide, Maleic Acid, and its use for Urea absorption

Hydrogel is a three-dimensional polymer that can absorb large amounts of reagents while maintaining structural integrity. This material has been applied in many fields especially in smart agriculture. To improve the economic viability, the reusability of hydrogels in agricultural engineering over multiple cycles of adsorption and desorption is an urgent requirement. This can be solved if the crosslinker is used properly. Therefore, in this work, a series of porous semi-IPN hydrogels based on linear polyacrylamide, acrylamide, maleic acid, and N,N′-methylenebisacrylamide (MBA) were synthesized. The hydrogels were evaluated for the impact of MBA content on the characteristics and applicability as a urea fertilizer carrier. The chemical composition, morphology, mechanical, and rheological properties, swelling behavior, urea absorption and desorption of hydrogels with crosslinker content in the range of 0.5-2.0 % were investigated. The porous structure was confirmed by scanning electron microscopy images. Changing the MBA content significantly affected all characteristics of the hydrogels. In particular, increasing the MBA content decreased the equilibrium swelling ratios in all investigated environments. The maximum amount of urea loaded into the hydrogel was also reduced from 435.88 to 188.50 mg/g. This increase also changed the swelling mechanism from non-Fickian to Fickian, whereas the urea release mechanism changed from Fickian to non-Fickian. Finally, the hydrogels demonstrated stability in soil over multiple cycles of water absorption and release. This study provides valuable insights into designing a semi-IPN hydrogel with desired properties that meet the application requirements of modern farming techniques.

A Photoelectric Synergistic Flexible Solid Slippery Surface for All-Day Anti-Icing/Frosting.

The accumulation of ice on surface has caused great harm to lots of fields such as transportation or aerospace. Nowadays, various equipment or tools used in low-temperature environments, which face the risk of interface icing, usually have irregular shapes. Traditional rigid anti-icing materials are difficult to meet practical application requirements. Thus, it is crucial to develop flexible anti-icing materials that can be applied to various shape surfaces (curved surfaces, flat surfaces). In this paper, a photoelectric synergistic flexible solid slippery surface (FSSS) is prepared by using flexible basalt fiberglass cloth, flexible copper foil, flexible polyurethane/carbon nanotubes mixture, and flexible solid lubricant (the mixture of coconut wax and coconut oil). Even under harsh conditions of the temperature as low as -80 °C, the FSSS exhibits excellent all-day anti/de-icing performance whether on flat or curved surface. Moreover, the FSSS shows long-term stability both on flat and curved surface: situated in air for 60 days, submerged in water for 60 days, kept in acid environment (pH 1) and base environment (pH 13) for 30 days. Besides, the FSSS can also achieve self-healing function under -80 °C. This flexible surface provides a novel approach for de-icing/frosting of multi-shaped objects in the future.

Review and Future Perspectives of Stimuli-Responsive Bridged Polysilsesquioxanes in Controlled Release Applications

Bridged polysilsesquioxanes (BPSs) are emerging biomaterials composed of synergistic inorganic and organic components. These materials have been investigated as ideal carriers for therapeutic and diagnostic systems for their favorable properties, including excellent biocompatibility, physiological inertia, tunable size and morphology, and their extensive design flexibility of functional organic groups to satisfy diverse application requirements. Stimuli-responsive BPSs can be activated by both endogenous and exogenous stimuli, offering a precise, safe, and effective platform for the controlled release of various targeted therapeutics. This review aims to provide a comprehensive overview of stimuli-responsive BPSs, focusing on their synthetic strategies, biocompatibility, and biodegradability, while critically assessing their capabilities for controlled release in response to specific stimuli. Furthermore, practical suggestions and future perspectives for the design and development of BPSs are presented. This review highlights the significant role of stimuli-responsive BPSs in advancing biomedical research.

Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection

This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. Deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical–classical deep learning architectures within 6G environments.

Reliability Analysis of Transmission Tower Based on Unscented Transformation Under Ice and Wind Loads

Due to the complexity of the transmission tower structure and the correlation between wind and ice loads in the actual project, it is difficult to analyze the reliability of transmission towers with traditional methods. To solve this problem, the unscented transformation (UT) principle is presented concisely and used in the reliability analysis of transmission towers in this paper. Moreover, the finite element model of the target transmission tower is created. The reliability indices of the transmission tower under various loading cases are evaluated using UT and analyzed relative to the outcomes of the Monte Carlo method (MCS). Lastly, by analyzing and validating a wine-cup shape tangent tower, the simulation results show that the UT yields reliability indices with less than 6% relative error compared with MCS results for the transmission towers with lower reliability, which are more important in engineering. Variations in error caused by the change in correlation coefficients among variables are small. Consequently, the efficiency of calculations is improved by the UT-based reliability calculations for transmission towers in the case of correlated variables, which better meet engineering application requirements. It is proved that the method of reliability analysis for transmission towers based on the UT is applicable.

Optimization of percutaneous intervention robotic system for skin insertion force.

Percutaneous puncture is a common interventional procedure, and its effectiveness is influenced by the insertion force of the needle. To optimize outcomes, we focus on reducing the peak force of the needle in the skin, aiming to apply this method to other tissue layers. We developed a clinical puncture system, setting and measuring various variables. We analyzed their effects, introduced admittance control, set thresholds, and adjusted parameters. Finally, we validated these methods to ensure their effectiveness. Our system meets application requirements. We assessed the impact of various variables on peak force and validated the effectiveness of the new method. Results show a reduction of about 50% in peak force compared to the maximum force condition and about 13% compared to the minimum force condition. Finally, we summarized the factors to consider when applying this method. To achieve peak force suppression, initial puncture variables should be set based on the trends in variable impact. Additionally, the factors of the new method should be introduced using these initial settings. When selecting these factors, the characteristics of the new method must also be considered. This process will help to better optimize peak puncture force.

The Application of Digital Construction Technology in the Process of Smart City Construction

Digital construction technology is essential to achieving high-quality construction in smart cities. This article introduces the application of digital construction technology in smart city construction from the background of smart cities, such as 3D modeling technology, digital twin technology, and artificial intelligence technology. This article conducts qualitative research through literature analysis, exploring the application process, significance, and value of digital construction technology in the process of smart city construction, then discussing its current situation and shortcomings and proposing suggestions. Digital construction technology has been extensively utilized in various fields such as intelligent manufacturing and smart cities and has been introduced into the construction industry. However, model accuracy, data effectiveness, real-time interaction, and comprehensiveness need to be improved, which cannot fulfill the requirements of deep-level applications. In the future, cities will further promote the upgrading of infrastructure and software for digital construction technology, as well as the integration and innovation of AI technology and digital construction technology. Smart cities are anticipated to reach an advanced level of city intelligence and achieve sustainable, efficient, and people-oriented high-quality city development.

Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio

Mobile communication is ubiquitous in everyday life. The fifth generation of mobile networks (5G) introduced 5G New Radio as a radio access technology that meets current bandwidth, quality, and application requirements. Network steganographic channels that hide secret message transfers in an innocent carrier communication are a particular threat in mobile communications as these channels are often used for malware, ransomware, and data leakage. We systematically analyze the protocol stack of the 5G–air interface for its susceptibility to network steganography, addressing both storage and timing channels. To ensure large coverage, we apply hiding patterns that collect the essential ideas used to create steganographic channels. Based on the results of this analysis, we design and implement a network covert storage channel, exploiting reserved bits in the header of the Packet Data Convergence Protocol (PDCP). the covert sender and receiver are located in a 5G base station and mobile device, respectively. Furthermore, we sketch a timing channel based on a recent overshadowing attack. We evaluate our steganographic storage channel both in simulation and real-world experiments with respect to steganographic bandwidth, robustness, and stealthiness. Moreover, we discuss countermeasures. Our implementation demonstrates the feasibility of a covert channel in 5G New Radio and the possibility of achieving large steganographic bandwidth for broadband transmissions. We also demonstrate that the detection of the channel by a network analyzer is possible, limiting its scope to application scenarios where operators are unaware or ignorant of this threat.

High-Efficiency Targeted Gene Transfection of Cells Using Temporal and Spatial Shaping Femtosecond Laser Irradiation.

Laser-irradiation-assisted cell gene transfection is sterile and nontoxic, but the low transfection efficiency cannot meet the application requirements. To improve the efficiency, a temporal and spatial shaping method of a femtosecond laser is proposed. Using the time shaping method, we can segment the pulse into subpulses of varying energies and with a defined delay, thereby influencing the interaction between electrons and photons, ultimately enhancing transfection efficiency. The transfection efficiency is further improved by spatially shaping the laser pulse to extend the focusing beam's working distance and reduce the cell's sensitivity to the focal position. Through the characterization of the viability and transfection efficiency of HEK-293T cells, the method achieved efficient and active transfection, with a maximum transfection efficiency of 45.1% and a cell survival rate of 93.6%. This method provides key technical support for femtosecond laser transfection and promotes its further application in clinical practice.

Engineering Strain-Defects to Enhance Enzymatic Therapy and Induce Ferroptosis.

The effect of mimetic enzyme catalysis is often limited by insufficient activity and a single therapy is not sufficient to meet the application requirements. In this study, a multifunctional nanozyme, MMSR-pS-PEG, is designed and fabricated by modifying poly (ethylene glycol) grafted phosphorylated serine (pS-PEG) on mesoporous hollow MnMoOx spheres, followed by loading sorafenib (SRF) into the pores. Strain engineering-induced oxygen defects endow the nanozyme with enhanced dual-enzymatic activity to mimic catalase and oxidase-like activities, which catalyze the conversion of endogenous H2O2 into oxygen and subsequently into superoxide ions in the acidic tumor microenvironment. Moreover, as an n-type semiconductor, MnMoOx generates reactive oxygen species by separating electrons and holes upon ultrasonic irradiation and simultaneously deplete glutathione by holes, thereby further augmenting its catalytic effect. As a ferroptosis inducer, SRF restrains the system xc - and indirectly inhibits glutathione synthesis, synergistically interacting with the nanozyme to stimulate ferroptosis by promoting lipid peroxidation and accumulation and the downregulation of glutathione peroxidase 4. These results provide valuable insights into the design of enzymatic therapy with high performance and highlight a promising approach for the synergism of ferroptosis and enzymatic tumor therapy.

A unified LOD model for river network and DEM based on an improved 3D_DP algorithm

The level of detail (LOD) modelling of vector and terrain data is individual, resulting in geometric and topological inconsistencies in simplification processes. The three dimension Douglas Peucker (3D_DP)algorithm can realize gradual discrete point selection through threshold, which is mainly used in DEM synthesis, and its simplified process is very suitable for the dynamic establishment of massive data sets. A new LOD modeling method based on 3D_DP algorithm is proposed to simplify the consistency of river network vector elements and DEM in this paper. The specific steps are as follows: Firstly, the “Bending Adjustment Index (BAI)” is introduced to improve the 3D_DP algorithm, called the improved 3D_DP algorithm; Secondly, the DEM data is extracted into a 3D discrete point dataset, and the river line vector data is also converted into a discrete point dataset, assigned with elevation attributes, and merges with the DEM’s 3D discrete points. The merged point datasets are equenced based on the importance of each point, which are computed by the improved 3D_DP algorithm. The order of deleting points is determined by the sequence and the corresponding model spatial errors are calculated with the deletion of points. Then, the DEM’s 3D discrete points are constructed a Delaunay network with the river line as a mandatory constraint condition. The required triangulation is called in real time with the change of sight distance depending on the simple correspondence between screen projection error and model space error, and the unified LOD model for river line vector and DEM is established. The results show that the river’s overall shape and the terrain’s main features can be reserved under the same simplified factor based on the improved 3D_DP algorithm. The unified LOD model for the river network and DEM is feasible under the importance sequence of merged point datasets by the improved 3D_DP algorithm. Under the proper operation of data blocking, the rendering frame rate can meet practical application requirements.

System Design of Three-coordinate Dynamic Synchronous Tester Based on STM32

In this paper, a three-coordinate dynamic synchronous tester system based on STM32 is developed. STM32F103C8T6 chip is used as the control system. The control system mainly consists of three modules: motion control module, magnetic encoder measurement module and displacement data collection module. The STM32 main control controls the three-axis stepper table to move according to the specified instructions, the AS5600 magnetic encoder is used to measure the data of the three-way stepper motor, and the TCA9548A module is used to expand the IIC channel and realize the communication between the STM32 and the host computer, and the data is transmitted to the host computer through serial communication. The test results show that the system meets the needs of the existing domestic industrial level, the cost is moderate, the performance is reliable and the operation is simple, and meets the practical application requirements.

Considerations for a Micromirror Array Optimized for Compressive Sensing (VIS to MIR) in Space Applications

Earth observation (EO) is crucial for addressing environmental and societal challenges, but it struggles with revisit times and spatial resolution. The EU-funded SURPRISE project aims to improve EO capabilities by studying space instrumentation using compressive sensing (CS) implemented through spatial light modulators (SLMs) based on micromirror arrays (MMAs) to improve the ground sampling distance. In the SURPRISE project, we studied the development of an MMA that meets the requirements of a CS-based geostationary instrument working in the visible (VIS) and mid-infrared (MIR) spectral ranges. This paper describes the optical simulation procedure and the results obtained for analyzing the performance of such an MMA with the goal of identifying a mirror design that would allow the device to meet the optical requirements of this specific application.

XAIP: An eXplainable AI‐Based Pipeline for Identifying Key Factors of Surface Defects in Strip Steel

The surface defect has a direct impact on the performance and quality of the final product, so it is crucial to identify the key factors causing the defect. To achieve accurate key factor identification, an eXplainable AI‐based Pipeline (XAIP) is proposed herein. The proposed XAIP combines data mode clustering and local model construction to meet practical application requirements. Meanwhile, after analyzing the characteristics of practical data, a strategy for defect rate calculation is designed to provide more fine‐grained defect‐related information to supervise model training. The final identification results are presented by a two‐stage explainable method, which is designed to reveal the information of data modes and key variables and can give engineers more specific defect‐related information. The experiment results based on two practical manufacturing datasets show the effectiveness of the proposed method.

Wide‐Spectrum Nano‐Antibiotics Based on TPA‐Py@AuNCs⊂BSA for Multimodal Synergistic Therapy of Drug‐Resistant Bacteria and Wound Infections

ABSTRACTTo meet the high requirements of biomedical applications in antimicrobial agents, it is crucial to explore efficient nano‐antimicrobial agents with no resistance and good biocompatibility for treating infected wounds. In this study, composite nano‐antibiotic TPA‐Py@AuNCs⊂BSA nanoparticles (TAB NPs) are prepared using hollow mesoporous Au nanocages (AuNCs) loaded with a photosensitizer (namely TPA‐Py) with D‐π‐A structure showing aggregation‐induced emission properties. When TPA‐Py is encapsulated in the cavity of AuNCs, its fluorescence is suppressed. In the presence of photothermal induction, TPA‐Py can be released from the AuNCs, allowing for the restoration of fluorescence illumination and the specific imaging of Gram‐positive bacteria. TAB NPs demonstrate outstanding antimicrobial activity against a variety of bacteria, and this multimodal antimicrobial property does not lead to the development of bacterial resistance. In vitro experiments show that TAB NPs could eliminate bacteria and ablate bacterial biofilm. In vivo experiments show that the synergistic antimicrobial effect of TAB NPs has a significant positive impact on the treatment of infected wounds, including rapid antibacterial action, promotion of M2 macrophage polarization, and enhancement of chronic wound healing. This study provides an effective strategy for developing wide‐spectrum nano‐antibiotics for the ablation of bacterial biofilms and the treatment of infected wounds.

Design of a low-power Digital-to-Pulse Converter (DPC) for in-memory-computing applications

Data converters such as analog-to-digital converters (ADCs), digital-to-analog converters (DACs), digital-to-time converters (DTCs), time-to-digital converters (TDCs), among others, are considered some of the most essential blocks in the field of integrated circuit design. In this work, we introduce a novel type of data converter known as the Digital-to-Pulse converter (DPC) and present its novel analog flow circuit implementation. The DPC system is a critical component in emerging artificial neural network accelerators and in-memory computing systems. The DPC system presented in this study offers two distinct operating modes. The first mode is the generation of a single pulse with a width that is modulated by the digital input. The second mode is an n-bit digital to discrete pulse converter, where the number of generated pulses is directly related to the value of the digital input. The proposed DPC system offers designers a high level of flexibility in shaping the characteristics of the output pulses, including the number of pulses, pulse width, and pulse amplitude. This empowers designers to accommodate different application requirements and scenarios effectively. The proposed circuit has been verified and tested using Virtuoso Cadence circuit tools in 180 nm CMOS technology with post-layout simulation and analysis. The results indicate a significant enhancement in average power consumption (∼12×), layout area (∼5×), and Latency (∼1.4×) with the proposed system compared to the digital Register Transfer Level (RTL) implementation under a power supply of 1.8V and a clock frequency of 1 GHz in the Application Specific Integrated Circuits (ASIC) flow. This demonstrates the suitability of the proposed system for low-power and high-speed applications.

Delay-aware resource allocation for partial computation offloading in mobile edge cloud computing

Mobile Edge Cloud Computing (MECC), as a promising partial computing offloading solution, has provided new possibilities for compute-intensive and delay-sensitive mobile applications, which can simultaneously leverage edge computing and cloud services. However, designing resource allocation strategies for MECC faces an extremely challenging problem of simultaneously satisfying the end-to-end latency requirements and minimum resource allocation of multiple mobile applications. To address this issue, we comprehensively consider the randomness of computing request arrivals, service time, and dynamic computing resources. We model the MECC network as a two-level tandem queue consisting of two sequential computing processing queues, each with multiple servers. We apply a deep reinforcement learning algorithm called Deep Deterministic Policy Gradient (DDPG) to learn the computing speed adjustment strategy for the tandem queue. This strategy ensures the end-to-end latency requirements of multiple mobile applications while preventing overuse of the total computing resources of edge servers and cloud servers. Numerous simulation experiments demonstrate that our approach is significantly superior to other methods in dynamic network environments.