Complex Application Scenarios Research Articles

Covalent Self-Assembly of Bio-HCP Nanoparticles for Shell-Programmed Encapsulation of Microbial Cells.

Modifying the bacterial surface through grafting functional nanoparticles is a common strategy for programing bacteria. At this moment, the targeted nanoparticles face a dilemma of no multifunctional structure, high toxicity, and weak chemical driving forces, which restrict the broad practical applications. Like a multistage booster of a rocket, we propose a multistage covalent self-assembly strategy to protect, expand, and control the encapsulated shells of microbial cells via biocompatible hyper-cross-linked polymer nanoparticles (Bio-HCP NPs) with internal porosity and surface functional groups. The bacterial surface is enhanced with rich amino groups up to 1010 per cell for specifically grafting nanoparticles. The arming bacteria after first-stage assembly can complete biocatalysis in a highly toxic environment, and as-prepared polymer aggregates (6-20 μm) after third-stage assembly can be accurately counted in an aerosol environment. This nanoparticle encapsulation exhibits strong cell viability from pollutants and specificity from impurity particles, holding promise for various complex application scenarios.

Joint Approach for Vehicle Routing Problems Based on Genetic Algorithm and Graph Convolutional Network

The logistics demands of industries represented by e-commerce have experienced explosive growth in recent years. Vehicle path-planning plays a crucial role in optimization systems for logistics and distribution. A path-planning scheme suitable for an actual scenario is the key to reducing costs and improving service efficiency in logistics industries. In complex application scenarios, however, it is difficult for conventional heuristic algorithms to ensure the quality of solutions for vehicle routing problems. This study proposes a joint approach based on the genetic algorithm and graph convolutional network for solving the capacitated vehicle routing problem with multiple distribution centers. First, we use the heuristic method to modularize the complex environment and encode each module based on the constraint conditions. Next, the graph convolutional network is adopted for feature embedding for the graph representation of the vehicle routing problem, and multiple decoders are used to increase the diversity of the solution space. Meanwhile, the REINFORCE algorithm with a baseline is employed to train the model, ensuring quick returns of high-quality solutions. Moreover, the fitness function is calculated based on the solution to each module, and the genetic algorithm is employed to seek the optimal solution on a global scale. Finally, the effectiveness of the proposed framework is validated through experiments at different scales and comparisons with other algorithms. The experimental results show that, compared to the single decoder GCN-based solving method, the method proposed in this paper improves the solving success rate to 100% across 15 generated instances. The average path length obtained is only 11% of the optimal solution produced by the GCN-based multi-decoder method.

A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model

Smart agriculture has become an inevitable trend in the development of modern agriculture, especially promoted by the continuous progress of large language models like chat generative pre-trained transformer (ChatGPT) and general language model (ChatGLM). Although these large models perform well in general knowledge learning, they still have certain limitations and errors when facing agricultural professional knowledge about crop disease identification, growth stage judgment, and so on. Agricultural data involves images and texts and other modalities, which play an important role in agricultural production and management. In order to better learn the characteristics of different modal data in agriculture, realize cross-modal data fusion, and thus understand complex application scenarios, we propose a framework AgriVLM that uses a large amount of agricultural data to fine-tune the visual language model to analyze agricultural data. It can fuse multimodal data and provide more comprehensive agricultural decision support. Specifically, it utilizes Q-former as a bridge between an image encoder and a language model to achieve a cross-modal fusion of agricultural images and text data. Then, we apply a Low-Rank adaptive to fine-tune the language model to achieve an alignment between agricultural image features and a pre-trained language model. The experimental results prove that AgriVLM demonstrates great performance in crop disease recognition and growth stage recognition, with recognition accuracy exceeding 90%, demonstrating its capability to analyze different modalities of agricultural data.

Lightweight and hierarchical carboxyl chitosan-derived carbon aerogel for electromagnetic wave absorber and heat insulation

In recent years, carbon aerogels derived from biomass have shown promising potential in electromagnetic wave (EMW) absorption field owing to their lightweight nature, controllable structure, environmental friendliness, and abundant pore structure. This study reports a novel type of carbon aerogels based on carboxyl chitosan (CCS) and graphene oxide (GO) with rich pores and a unique three-dimensional interconnected structure through solution mixing, freeze-drying, and pyrolysis process. The EMW absorption performances of the carboxyl chitosan/GO xerogel derived carbon aerogels (CGCAs) are adjusted by varying the content of graphene oxide (GO). The nitrogen elements inherent in chitosan can provide a large number of defects to the carbonized material, effectively capturing and attenuating EMW. Synthesized CGCAs show excellent EMW absorption performance with increasing GO content and the CGCA-6 exhibits the best performance. At a thickness of 1.7 mm, it has RLmin of −61.67 dB, and at a thickness of 1.5 mm, it has the widest effective absorption bandwidth (EAB) of 4.24 GHz (12.96–17.12 GHz). Additionally, under far-field conditions, at a thickness of 1.7 mm, the maximum radar cross-section (RCS) reduction relative to a PEC plate for the prepared CGCA-6 is 22.28 dB m2. Furthermore, the good thermal insulation capability exhibited by CGCA-6 makes it suitable for complex application scenarios. Therefore, this biomass-derived multi-functional carbon aerogel with excellent EMW absorption performance, radar stealth, infrared stealth, and thermal insulation capabilities has broad prospects for applications in EMW protection and aerospace fields.

A novel quantum security multi-party extremum protocol in a d-dimensional quantum system

Secure multi-party extremum computation (SMEC) is a specific application scenario of secure multi-party computation, which allows multiple participants to compute the extremum of data without disclosing private information. The extremum includes maximum, minimum, sum of extremums, and difference of extremums. SMEC has wide applications in financial transactions, market analysis, sports events, healthcare, etc. Current protocol research mainly exists in the classical domain and cannot withstand quantum computing attacks. To address this issue, we propose a novel QSME protocol based on a d-dimensional quantum system, capable of computing the maximum and minimum values among multi-party data under unconditional security, and can compute the sum and difference of extremums without disclosing the maximum and minimum values, to adapt to complex application scenarios. The article proposes a coding method for a d-dimensional quantum system to further enhance security, provides correctness analysis, security analysis, robustness analysis, and comparative analysis, and proposes an experimental method for a d-dimensional quantum system to verify the effectiveness of the protocol, demonstrating strong practicality.

NUMERICAL INVESTIGATION OF A OMNIDIRECTIONAL BENDING ACTUATOR FOR CLIMBING ANALYSIS

With its good flexibility and adaptability to the environment, soft robot has been attached great importance by the majority of researchers in recent years, and is widely used in rehabilitation treatment, flexible grasping and other fields. However, the existing actuators have disadvantages such as single deformation mode, poor flexibility and insufficient environmental adaptability. The reason is that the actuator structure is too simple to meet the complex application scenarios. In this paper, an omnidirectional bending multi-segment soft actuator is proposed, and the parameter analysis and dynamic analysis models are given. Meanwhile, the prestressed modal analysis of the proposed model is carried out to better characterize the dynamic behavior. A numerical study of a climbing motion verifies the novelty and validity of the proposed structure.

Topology-preserved distorted space path planning

Purpose Fast obstacle avoidance path planning is a challenging task for multijoint robots navigating through cluttered workspaces. This paper aims to address this issue by proposing an improved path-planning method based on the distorted space (DS) method, specifically designed for high-dimensional complex environments. Design/methodology/approach The proposed method, termed topology-preserved distorted space (TP-DS) method, mitigates the limitations of the original DS method by preserving space topology through elastic deformation. By applying distinct spring constants, the TP-DS autonomously shrinks obstacles to microscopic areas within the configuration space, maintaining consistent topology. This enhancement extends the application scope of the DS method to handle complex environments effectively. Findings Comparative analysis demonstrates that the proposed TP-DS method outperforms traditional methods regarding planning efficiency. Successful obstacle avoidance tasks in the cluttered workspace validate its applicability on a physical 6-DOF manipulator, highlighting its potential for industrial implementations. Originality/value The novel TP-DS method generates a topology-preserved collision-free space by leveraging elastic deformation and shows significant capability and efficiency in planning obstacle-avoidance paths in complex application scenarios.

Polyvinyl alcohol/PEDOT:PSS with Fe3+/amylopectin enabled highly tough, anti-freezing and healable hydrogels for multifunctional wearable sensors

In recent years, hydrogel-based flexible sensors have garnered increasing attention in research. Ionic hydrogels, enriched with large amounts of ionic liquids, exhibit electrical conductivity, excellent electrochemical stability, anti-freezing, and antimicrobial properties. However, most ionic hydrogels suffer from poor mechanical properties, limiting their adaptability to more complex application scenarios. Integrating conductive polymers into hydrogels leads to desirable features such as increased specific surface area, soft and biocompatible interfaces, and high electrolyte permeability. In this study, we successfully prepared Fe3+/Ap@PVA/PEDOT double-network hydrogel. Utilizing polyvinyl alcohol (PVA) as the primary matrix, we introduced PEDOT:PSS and FeCl3 to confer conductivity to the hydrogel. The incorporation of amylopectin (Ap) further enhanced mechanical performance. The resulted hydrogel sensor exhibits outstanding mechanical properties, allowing for stretching up to 347 % and withstanding a tensile force of 505 kPa. In addition, it exhibits excellent antifreeze properties (can work at −30 °C), healability, water retention, and high sensitivity to stretching (GF = 4.72 at a 200 % strain ratio), compression (GF = 2.97 at a 12 % compressive ratio), and temperature (TCR = 2.46). These remarkable properties of the hydrogel make it possible in applications such as human motion monitoring, handwriting recognition, and temperature sensing.

Wavelength‐Selective Photodetector and Neuromorphic Visual Sensor Utilizing Intrinsic Defect Semiconductor

AbstractWith the rapid developments of Artificial Intelligence (AI) and the Internet of Things (IoT), increasingly intricate and expanding application scenarios are placing higher demands on current machine vision capabilities. Therefore, there is a pressing need to simultaneously achieve diverse functionalities, simple designs, and efficient computing in vision devices. Here, the study develops a two‐terminal optoelectronic device utilizing a single 2D intrinsic defect semiconductor In2S3. The device demonstrates wavelength‐selective photodetection and neuromorphic visual capabilities, attributed to defect‐related charge‐trapping/de‐trapping processes. As a photodetector, the device exhibits a high photoresponsivity of 473.6 A W−1, a high external quantum efficiency of 1.6 × 105%, and a fast rise/fall time of 0.3/1.4 ms at the wavelength of 359 nm. As an all‐in‐one neuromorphic visual device, optoelectronic‐driven fundamental synaptic functions, including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP), and “learning‐experience”, are successfully mimicked at the wavelength of 671 nm. Moreover, one‐shot recognition of the 12‐letter image “SHAN XI NORMAL” is achieved through an artificial convolutional neural network. This study provides a new strategy for developing compact high‐level intelligence systems for complex application scenarios.

A Deep-Learning-Based Method for Spectrum Sensing with Multiple Feature Combination

Cognitive radio networks enable the detection and opportunistic access to an idle spectrum through spectrum-sensing technologies, thus providing services to secondary users. However, at a low signal-to-noise ratio (SNR), existing spectrum-sensing methods, such as energy statistics and cyclostationary detection, tend to fail or become overly complex, limiting their sensing accuracy in complex application scenarios. In recent years, the integration of deep learning with wireless communications has shown significant potential. Utilizing neural networks to learn the statistical characteristics of signals can effectively adapt to the changing communication environment. To enhance spectrum-sensing performance under low-SNR conditions, this paper proposes a deep-learning-based spectrum-sensing method that combines multiple signal features, including energy statistics, power spectrum, cyclostationarity, and I/Q components. The proposed method used these combined features to form a specific matrix, which was then efficiently learned and detected through the designed ‘SenseNet’ network. Experimental results showed that at an SNR of −20 dB, the SenseNet model achieved a 58.8% spectrum-sensing accuracy, which is a 3.3% improvement over the existing convolutional neural network model.

A bacteria-based index of biotic integrity indicates aquatic ecosystem restoration

Intensive ecological interventions have been carried out in highly polluted shallow lakes to improve their environments and restore their ecosystems. However, certain treatments, such as dredging polluted sediment and stocking fish, can impact the aquatic communities, including benthos and fishes. These impacts can alter the composition and characteristics of aquatic communities, which makes community-based ecological assessments challenging. Here we develop a bacteria-based index of biotic integrity (IBI) that can clearly indicate the restoration of aquatic ecosystems with minimal artificial interventions. We applied this method to a restored shallow lake during 3-year intensive ecological interventions. The interventions reduced nutrients and heavy metals by 27.1% and 16.7% in the sediment, while the total organic carbon (TOC) increased by 8.0% due to the proliferation of macrophytes. Additionally, the abundance of sulfur-related metabolic pathways decreased by 10.5% as the responses to improved ecosystem. The score of bacteria-based IBI, which is calculated based on the diversity, composition, and function of benthic bacterial communities, increased from 0.62 in 2018 to 0.81 in 2021. Our study not only provides an applicable method for aquatic ecological assessment under intensive artificial interventions but also extends the application of IBI to complex application scenarios, such as ecosystems with significantly different aquatic communities and comparisons between different basins.

Ultra-sensitive detection of femtomolar lead ions using DNAzyme-mediated biosensing on tilted fiber Bragg grating plasmonic sensor

Ultra-trace lead ion detection has significant implications in various domains, notably in point-of-care testing, environmental monitoring, and cancer-targeted therapies and diagnostics. The demand for rapid and precise lead-ion detection in clinical settings necessitates innovative approaches. In this study, a lead-ion biosensor was designed using DNAzyme-based tilted fiber Bragg grating surface plasmon resonance (TFBG-SPR). The lead ions specifically cleave toward DNAzyme’s ribonucleotide adenosine (rA)-containing substrate strand, and a high sensitivity “on–off” sensing structure on the TFBG-SPR surface was established by using the DNA-conjugated AuNPs as a signal amplification element. This “on–off”-type lead ion biosensor has a competitive limit of detection of 3 fM and a wide dynamic measurement range up to ten orders, meeting stringent requirements for ultra-trace, label-free and online clinical detection. Owing to the unique catalytic effect of DNAzyme binding to lead ions, high selectivity was demonstrated. Moreover, to validate its practicality in clinical diagnosis, we successfully achieved spiked-recovery results with 100.39 % average recovery rate and a swift 17-minute analysis time within the colorectal cancer patient samples. By combining DNA biomolecular, TFBG-SPR probe and “on–off” AuNPs-DNA structure, the fM level detection of lead ion in fiber highlighted its potential in vivo and in vitro complex application scenarios.

Knowledge correction and [formula omitted]-insensitive criterion-leveraged zero-order TSK fuzzy system for rice leaf disease diagnosis

The advent of complex application scenarios introduces new challenges for diagnosing rice leaf diseases using machine learning methods. Two critical requirements are identified: 1) The model must exhibit high interpretability to mitigate the adverse effects of incorrect diagnoses; and 2) practical applications often suffer from insufficient samples and noise in rice leaf disease datasets, which requires the model to have strong generalization ability and robustness. However, existing methods still have certain limitations in practical scenarios due to a lack of comprehensive consideration of interpretability, generalization ability, and robustness. To address this issue, this article proposes a novel knowledge correction and ε-insensitive criterion-leveraged zero-order TSK fuzzy system (0-TSK-FS), named KE-0-TSK-FS. The KE-0-TSK-FS method is developed with 0-TSK-FS as the baseline, enhancing the generalization ability of the model by introducing the knowledge correction method and its iterative learning strategy to extract more information from limited samples. In addition, the objective function based on the ε-insensitive criterion makes KE-0-TSK-FS exhibit robustness when the samples contain noise. On three rice leaf disease datasets and six real-world non-rice leaf disease datasets, experiments were conducted on three metrics, namely accuracy, GM, and rule complexity. The experimental results show that the KE-0-TSK-FS method outperforms other comparative algorithms in terms of generalization ability, interpretability, and robustness in the diagnosis of rice leaf diseases under insufficient samples and noise situations, and its average accuracy on rice leaf disease datasets is nearly 3% higher than that of other comparative algorithms.

Symmetrical Modeling of Physical Properties of Flexible Structure of Silicone Materials for Control of Pneumatic Soft Actuators

With the ongoing advancements in material technology, the domain of soft robotics has garnered increasing attention. Soft robots, in contrast to their rigid counterparts, offer superior adaptability to the environment, enhanced flexibility, and improved safety, rendering them highly suitable for complex application scenarios such as rescue operations and medical interventions. In this paper, a new type of pneumatic software actuator is proposed. The actuator adopts a combination of a soft structure and pneumatic control, which is highly flexible and versatile. By using the flow of gas inside the soft structure, high-precision and flexible motion control is realized. In the design process, the extensibility and adaptability of the structure are considered, so that the actuator can adapt to different working environments and task requirements. The experimental results show that the pneumatic soft actuator exhibits excellent performance in terms of accuracy, response speed, and controllability. This research provides new ideas and methods for the development of the field of pneumatic actuators and has wide application prospects. The main research content of this paper is as follows: first, the soft pneumatic actuator is modeled and simulated, the structure is optimized on the basis of simulation, and finally, the performance of the actuator is tested.

Voice interactive information metasurface system for simultaneous wireless information transmission and power transfer

Intelligent voice interaction offers a flexible and powerful way to connect individuals with smart devices beyond our expectations. The real-time nature of voice communication enables smart devices to comprehend the user language, execute the corresponding instructions, and facilitate seamless communications, transforming our lives in unprecedented ways. Owing to self-adaptive and reprogrammable functionalities, information metasurface (IMS) opens up a new avenue for smart home and smart cities. To further enhance the intelligence of IMS, we propose an IMS system via intelligent voice interaction and information processing. The voice interaction enables the efficient remote control on the IMS in a flexible, convenient, touchless manner. Leveraging speech recognition, speech synthesis, target detection, and communication technologies, the IMS system achieves automatic beam manipulation capabilities for wireless information transmissions and wireless power transfers. The IMS system is designed to operate in two distinct modes: instruction mode, wherein the user instructs the operations, and autonomous mode, wherein the automatic detections govern the actions, in which seamless mode switching through the voice commands is supported. Users can flexibly achieve precise control over the functions of the intelligent metasurface system through voice interaction at a distance, without the need for close-range manual touch control, which greatly simplifies the operation difficulty and is particularly suitable for remote control and complex application scenarios. A series of experiments, including wireless video transmissions and wireless power transfers are conducted to demonstrate the flexibility and convenience of the IMS system. The incorporation of intelligent voice interaction technology with the IMS presents a novel paradigm for the applications of programmable and information metasurfaces.

An improved GNSS ambiguity best integer equivariant estimation method with Laplacian distribution for urban low-cost RTK positioning

The integer least squares (ILS) estimation is commonly used for carrier phase ambiguity resolution (AR). More recently, the best integer equivariant (BIE) estimation has also attracted an attention for complex application scenarios, which exhibits higher reliability by a weighted fusion of integer candidates. However, traditional BIE estimation with Gaussian distribution (GBIE) faces challenges in fully utilizing the advantages of BIE for urban low-cost positioning, mainly due to the presence of outliers and unmodeled errors. To this end, an improved BIE estimation method with Laplacian distribution (LBIE) is proposed, and several key issues are discussed, including the weight function of LBIE, determination of the candidates included based on the OIA test, and derivation of the variance of LBIE solutions for reliability evaluation. The results show that the proposed LBIE method has the positioning accuracy similar to the BIE using multivariate t-distribution (TBIE), and significantly outperforms the ILS-PAR and GBIE methods. In an urban expressway test with a Huawei Mate40 smartphone, the LBIE method has positioning errors of less than 0.5 m in three directions and obtains over 50% improvements compared to the ILS-PAR and GBIE methods. In an urban canyon test with a low-cost receiver STA8100 produced by STMicroelectronics, the positioning accuracy of LBIE in three directions is 0.112 m, 0.107 m, and 0.252 m, respectively, with improvements of 17.6%, 27.2%, and 26.1% compared to GBIE, and 23.3%, 28.2%, and 30.6% compared to ILS-PAR. Moreover, its computational time increases by 30–40% compared to ILS-PAR and is approximately half of that using TBIE.

Preparation and properties of fluorosilicone composites with thermal conductivity and chemical resistance through modification of filler and matrix

The improvement of heat dissipation and chemical resistance of encapsulating materials can greatly increase the service life of electronic devices. Fluorosilicone rubber exhibits superior chemical resistance compared to silicone rubber when used for encapsulation, enabling it to effectively handle highly complex application scenarios. In this study, a thermally conductive and chemically resistant composite material (i.e., F-Al2O3/F-AlN@F-ALSR) was prepared by introducing fluorine-modified Al2O3 and AlN into fluorine-added liquid silicone rubber matrix from both filler and matrix fluorination modification perspectives. The effects of the properties of fillers before and after modification, different mixing ratios (i.e., the ratio of F-Al2O3 to F-AlN) and different addition amounts (i.e., the amount of F-Al2O3/F-AlN added in F-ALSR) on the thermal conductivity and chemical medium resistance of the composite material were investigated, and the mechanisms of thermal conductivity and chemical medium resistance were explored. The results showed that when the ratio of F-Al2O3 to F-AlN was 3:7 and the total amount of filling was 300 phr, the material had a thermal conductivity of 1.047 W/mK and a thermal diffusivity of 0.652 mm2/s. The rate of change of mass and volume during 72 h of immersion in acid, alkali, toluene and engine oil was low. This further proves that the addition of F-Al2O3/F-AlN in F-ALSR can effectively create a heat conduction path and protect the composite material from environmental effects, resulting in materials with excellent thermal conductivity and chemical resistance. This study provides new ideas for improving the dispersion of fillers in the matrix and rationalizing the preparation of liquid silicone rubbers for thermally conductive, chemically resistant electronic potting.

Fault diagnosis method of PEMFC system based on ensemble learning

Due to the coupling of multiple physical fields inside proton exchange membrane fuel cell (PEMFC) and the complex and changeable external application scenarios, the faults of PEMFC system greatly affects its engineering application. The paper proposes an ensemble learning-based fault diagnosis method for PEMFC systems, utilizing five distinct algorithms to develop an integrated ensemble classifier for fault detection. The original sample dataset collected from fuel cells undergoes a process of deep feature extraction to obtain a composite feature sample dataset, which is then inputted into an ensemble classifier to establish a fault diagnosis model. The experimental data of four kinds of faults of 80 W fuel cell test bench, EC fuel cell system and 50 W water-cooled fuel cell test bench are analyzed by using this classification model. The results show that the accuracy and generalization of the ensemble learning algorithm for different fault diagnosis of PEMFC system in different application environments are verified.

A Soft Robot Tactile Finger Using Oxidation-Reduction Graphene-Polyurethane Conductive Sponge.

Currently, intelligent robotics is supplanting traditional industrial applications. It extends to business, service and care industries, and other fields. Stable robot grasping is a necessary prerequisite for all kinds of complex application scenarios. Herein, we propose a method for preparing an elastic porous material with adjustable conductivity, hardness, and elastic modulus. Based on this, we design a soft robot tactile fingertip that is gentle, highly sensitive, and has an adjustable range. It has excellent sensitivity (~1.089 kpa-1), fast response time (~35 ms), and measures minimum pressures up to 0.02 N and stability over 500 cycles. The baseline capacitance of a sensor of the same size can be increased by a factor of 5-6, and graphene adheres better to polyurethane sponge and has good shock absorption. In addition, we demonstrated the application of the tactile fingertip to a two-finger manipulator to achieve stable grasping. In this paper, we demonstrate the great potential of the soft robot tactile finger in the field of adaptive grasping for intelligent robots.

A soft robot tactile fingertip for grasping surface posture detection

Abstract The application field of robots is gradually extending from traditional industrial manufacturing to commercial services, medical care and other fields. Stable grasping is a necessary prerequisite for various complex robot application scenarios. The existing research on robot tactile sensor mainly focus on sensing tactile information, such as pressure or sliding. However, grasping surface posture as a tactile information that has a significant impact on the stability of grasping is often ignored. This study proposed a novel soft robot tactile fingertip that can not only detect the grasping force but also simultaneously detect the grasping surface posture. It is capable of recognizing 19 grasping surface postures in real-time using Mamdani fuzzy control method. The soft robot tactile fingertip has a simple structure and easy to fabricate at low cost. The application example of the two-finger mechanical hand using the soft tactile fingertip samples for stable grasping is demonstrated. This study shows that the soft robot tactile fingertip has great application prospects in the filed of robot intelligent grasping.