Functional Modules Research Articles

Channel-aware low Earth orbit satellite cluster networking for space-to-Earth laser communication: reliability and bandwidth advantages.

The reliability of the space-to-Earth laser communication plays a crucial role in providing uninterrupted real-time services in satellite optical networks. In traditional satellite optical networks, the space-to-Earth laser communication is carried out using a monolithic satellite in close proximity to the target optical ground station. However, the reliability of the communication in this approach is heavily influenced by the atmospheric environment. For instance, variations in cloud thickness can cause fluctuations in the link quality of the space-to-Earth laser communication, significantly reducing its reliability. This study proposes an innovative channel-adaptive space-to-Earth laser communication (CA-S2E-LC) architecture based on satellite cluster optical networking (SCON). SCON provides space-diversity link sets, reducing the probability of space-to-Earth laser communications affected by clouds. By leveraging the perception of link quality, the CA-S2E-LC architecture can adaptively choose the better space-to-Earth laser communication links established by member satellites within a satellite cluster under different environments, and properly schedule the resource, ensuring reliable space-to-Earth laser communication. The principles of the SCON is analyzed and the implementation of the CA-S2E-LC architecture is demonstrated through the explanation of hardware and functional modules, workflows, finite state machines, and strategies. Simulation results demonstrate that the CA-S2E-LC architecture can significantly enhance communication reliability and capacity compared with the traditional monolithic satellite. Furthermore, the workflow of the architecture is demonstrated to validate the feasibility.

Weed detection in soybean fields using improved YOLOv7 and evaluating herbicide reduction efficacy.

With the increasing environmental awareness and the demand for sustainable agriculture, herbicide reduction has become an important goal. Accurate and efficient weed detection in soybean fields is the key to test the effectiveness of herbicide application, but current technologies and methods still have some problems in terms of accuracy and efficiency, such as relying on manual detection and poor adaptability to some complex environments. Therefore, in this study, weeding experiments in soybean fields with reduced herbicide application, including four levels, were carried out, and an unmanned aerial vehicle (UAV) was utilized to obtain field images. We proposed a weed detection model-YOLOv7-FWeed-based on improved YOLOv7, adopted F-ReLU as the activation function of the convolution module, and added the MaxPool multihead self-attention (M-MHSA) module to enhance the recognition accuracy of weeds. We continuously monitored changes in soybean leaf area and dry matter weight after herbicide reduction as a reflection of soybean growth at optimal herbicide application levels. The results showed that the herbicide application level of electrostatic spraying + 10% reduction could be used for weeding in soybean fields, and YOLOv7-FWeed was higher than YOLOv7 and YOLOv7-enhanced in all the evaluation indexes. The precision of the model was 0.9496, the recall was 0.9125, the F1 was 0.9307, and the mAP was 0.9662. The results of continuous monitoring of soybean leaf area and dry matter weight showed that herbicide reduction could effectively control weed growth and would not hinder soybean growth. This study can provide a more accurate, efficient, and intelligent solution for weed detection in soybean fields, thus promoting herbicide reduction and providing guidance for exploring efficient herbicide application techniques.

Construction of a smart face recognition model for university libraries based on FaceNet-MMAR algorithm.

The continuous development of science and technology has led to the gradual digitization and intelligence of campus construction. To apply facial recognition technology to construct smart libraries in higher education, this study optimizes traditional facial recognition algorithm models. Firstly, a smart management system for university libraries is designed with facial recognition as the core, and secondly, the traditional FaceNet network is optimized. Combined with MobileNet, Attention mechanism, Receptive field module and Mish activation function, the improved multitask face recognition convolutional neural network is built and used in the construction of university smart library. The performance verification of the constructed model shows that the feature matching error value of the model in a stable state is only 0.04. The recognition accuracy in the dataset is as high as 99.05%, with a recognition error as low as 0.51%. The facial recognition model used in university smart libraries can achieve 97.6% teacher satisfaction and 96.8% student satisfaction. In summary, the facial recognition model constructed by this paper has good recognition performance and can provide effective technical support for the construction of smart libraries.

An optimized fuzzy based FP-growth algorithm for mining temporal data

Data mining is one of the emerging technologies used in many applications such as Market analysis and Machine learning. Temporal data mining is used to get a clear knowledge about current trend and to predict the upcoming future. The rudimentary challenge in introducing a data mining procedure is, processing time and memory consumption are highly increasing while trying to improve the accuracy, precision or recall. As well as, while trying to reduce the processing time or memory consumption, accuracy, precision and recall values are reducing significantly. So, for improving the performance of the system and to preserve the memory and processing time, Three-Dimensional Fuzzy FP-Tree (TDFFPT) is proposed for Temporal data mining. Three functional modules namely, Three-Dimensional Temporal data FP-Tree (TTDFPT), Fuzzy Logic based Temporal Data Tree Analyzer (FTDTA) and Temporal Data Frequent Itemset Miner (TDFIM) are integrated in the proposed method. This algorithm scans the database and generates frequent patterns as per the business need. Every time a client purchases a new item, it gets stored in the recent database layer instead of rescanning the entire records which are placed in the old layer. The results obtained shows that the performance of the proposed model is more efficient than that of the existing algorithm in terms of overall accuracy, processing time, reduction in the memory utilization, and the number of databases scans. In addition, the proposed model also provides improved decision making and accurate pattern prediction in the time series data.

Weed detection and removal using robotic system

Weeding is one of the most significant practices in agricultural production. Weeds are unwanted plants that grow along with the crops and compete with the crops for space, light, water, and soil nutrients. Weeds propagate themselves either through seeding or creeping rootstalk and decrease yields, increase production costs, interfere with the harvest, and lower the product quality. The use of herbicides reduces labor requirements for weed control by up to 60 percent but affects environmental quality and can be toxic to a wide range of organisms. Hence it is necessary to develop an automated system to identify and remove weeds from the vegetable fields. The objective of the proposed work is to develop a mobility level tracked bot that identifies the weeds and removes them with the help of a robotic end effector and to develop a machine learning model to identify the weeds. This functional module will be processed in a Raspberry Pi processor and by using a Raspberry Pi camera module the bot will detect the weeds in vegetable fields. We performed weed detection with different machine learning models like Haar cascade, YOLOv5, and CNN. To evaluate the performance of the machine learning models used, the performance metrics accuracy, precision, recall, and F-measure are estimated and it has been found that CNN has better accuracy, precision, and recall as compared to YOLOv5 and Haar cascade. CNN has the highest F-measure among the three algorithms at 98%. The weed removal is done using a robotic end-effector which is controlled by the Arduino UNO based on the signal from Raspberry Pi.

Genomic and biological characteristics of a novel phage induced from Limosilactobacillus fermentum

Bacteriophage infection of starter cultures remains a major threat to the food fermentation industry. To date, adequate information regarding the genome and characteristics of the Limosilactobacillus fermentum (L. fermentum) phage is lacking. The phage LFP01 was obtained by inducing L. fermentum IMAU32646 with mitomycin-C. The results showed that this phage contained a linear double-stranded DNA genome spanning 40,562 bp with a GC content of 46.25%. It comprises 54 coding sequences, which are grouped into three potential functional modules: DNA replication, cell lysogeny/lysis, and virion morphogenesis. Taxonomic analysis further confirmed that this phage belonged to a novel virus genus within the Caudoviricetes class. The optimal multiplicity of infection was 0.1, the latent period was 45 min, and the burst size was 158 ± 2.65 PFU/cell. This phage exhibited good environmental tolerance at 0–42 °C and a pH of 4–11. High temperature (50 °C) and strongly acidic (pH 2–3) conditions significantly influenced the adsorption capacity of the phage, but divalent cations and chloramphenicol had no similar effect. The findings of this study could enrich the database of phages and broaden the understanding of phage diversity.

Channel-Boosted and Transfer Learning Convolutional Neural Network-Based Osteoporosis Detection from CT Scan, Dual X-Ray, and X-Ray Images.

Osteoporosis is a word used to describe a condition in which bone density has been diminished as a result of inadequate bone tissue development to counteract the elimination of old bone tissue. Osteoporosis diagnosis is made possible by the use of medical imaging technologies such as CT scans, dual X-ray, and X-ray images. In practice, there are various osteoporosis diagnostic methods that may be performed with a single imaging modality to aid in the diagnosis of the disease. The proposed study is to develop a framework, that is, to aid in the diagnosis of osteoporosis which agrees to all of these CT scans, X-ray, and dual X-ray imaging modalities. The framework will be implemented in the near future. The proposed work, CBTCNNOD, is the integration of 3 functional modules. The functional modules are a bilinear filter, grey-level zone length matrix, and CB-CNN. It is constructed in a manner that can provide crisp osteoporosis diagnostic reports based on the images that are fed into the system. All 3 modules work together to improve the performance of the proposed approach, CBTCNNOD, in terms of accuracy by 10.38%, 10.16%, 7.86%, and 14.32%; precision by 11.09%, 9.08%, 10.01%, and 16.51%; sensitivity by 9.77%, 10.74%, 6.20%, and 12.78%; and specificity by 11.01%, 9.52%, 9.5%, and 15.84%, while requiring less processing time of 33.52%, 17.79%, 23.34%, and 10.86%, when compared to the existing techniques of RCETA, BMCOFA, BACBCT, and XSFCV, respectively.

Modularized Design and Construction of Tunable Microbial Consortia with Flexible Topologies.

Complex and fluid bacterial community compositions are critical to diversity, stability, and function. However, quantitative and mechanistic descriptions of the dynamics of such compositions are still lacking. Here, we develop a modularized design framework that allows for bottom-up construction and the study of synthetic bacterial consortia with different topologies. We showcase the microbial consortia design and building process by constructing amensalism and competition consortia using only genetic circuit modules to engineer different strains to form the community. Functions of modules and hosting strains are validated and quantified to calibrate dynamic parameters, which are then directly fed into a full mechanistic model to accurately predict consortia composition dynamics for both amensalism and competition without further fitting. More importantly, such quantitative understanding successfully identifies the experimental conditions to achieve coexistence composition dynamics. These results illustrate the process of both computationally and experimentally building up bacteria consortia complexity and hence achieve robust control of such fluid systems.

A spatial transformation-based CAN model for information integration within grid cell modules

The hippocampal-entorhinal circuit is considered to play an important role in the spatial cognition of animals. However, the mechanism of the information flow within the circuit and its contribution to the function of the grid-cell module are still topics of discussion. Prevailing theories suggest that grid cells are primarily influenced by self-motion inputs from the Medial Entorhinal Cortex, with place cells serving a secondary role by contributing to the visual calibration of grid cells. However, recent evidence suggests that both self-motion inputs and visual cues may collaboratively contribute to the formation of grid-like patterns. In this paper, we introduce a novel Continuous Attractor Network model based on a spatial transformation mechanism. This mechanism enables the integration of self-motion inputs and visual cues within grid-cell modules, synergistically driving the formation of grid-like patterns. From the perspective of individual neurons within the network, our model successfully replicates grid firing patterns. From the view of neural population activity within the network, the network can form and drive the activated bump, which describes the characteristic feature of grid-cell modules, namely, path integration. Through further exploration and experimentation, our model can exhibit significant performance in path integration. This study provides a new insight into understanding the mechanism of how the self-motion and visual inputs contribute to the neural activity within grid-cell modules. Furthermore, it provides theoretical support for achieving accurate path integration, which holds substantial implications for various applications requiring spatial navigation and mapping.

Synchronization-Inspired Interpretable Neural Networks.

Synchronization is a ubiquitous phenomenon in nature that enables the orderly presentation of information. In the human brain, for instance, functional modules such as the visual, motor, and language cortices form through neuronal synchronization. Inspired by biological brains and previous neuroscience studies, we propose an interpretable neural network incorporating a synchronization mechanism. The basic idea is to constrain each neuron, such as a convolution filter, to capture a single semantic pattern while synchronizing similar neurons to facilitate the formation of interpretable functional modules. Specifically, we regularize the activation map of a neuron to surround its focus position of the activated pattern in a sample. Moreover, neurons locally interact with each other, and similar ones are synchronized together during the training phase adaptively. Such local aggregation preserves the globally distributed representation nature of the neural network model, enabling a reasonably interpretable representation. To analyze the neuron interpretability comprehensively, we introduce a series of novel evaluation metrics from multiple aspects. Qualitative and quantitative experiments demonstrate that the proposed method outperforms many state-of-the-art algorithms in terms of interpretability. The resulting synchronized functional modules show module consistency across data and semantic specificity within modules.

Fractals in Neuroanatomy and Basic Neurosciences: An Overview.

The introduction of fractal geometry to the neurosciences has been a major paradigm shift over the last decades as it has helped overcome approximations and limitations that occur when Euclidean and reductionist approaches are used to analyze neurons or the entire brain. Fractal geometry allows for quantitative analysis and description of the geometric complexity of the brain, from its single units to the neuronal networks.As illustrated in the second section of this book, fractal analysis provides a quantitative tool for the study of the morphology of brain cells (i.e., neurons and microglia) and its components (e.g., dendritic trees, synapses), as well as the brain structure itself (cortex, functional modules, neuronal networks). The self-similar logic which generates and shapes the different hierarchical systems of the brain and even some structures related to its "container," that is, the cranial sutures on the skull, is widely discussed in the following chapters, with a link between the applications of fractal analysis to the neuroanatomy and basic neurosciences to the clinical applications discussed in the third section.

Design and Implementation of a Mobile Experimental Application for Networked Control Systems

With the development of mobile technologies, mobile applications are increasingly designed and developed. In the field of science, technology, engineering, and mathematics (STEM) education, the idea of using mobile devices for teaching and learning has gradually gained popularity and gained in-depth research. Owing to the advantages of convenience, portability and excellent interactive abilities, mobile devices are a good choice for accessing remote laboratories. In propose of promoting the massive online experimentation of control education, a mobile application with framework based on React-Native is implemented in this paper. The overall remote laboratory architecture adopts front-end and back-end separation scheme, which benefits the mobile application focusing on the construction of front-end interface and user experience. Various functional modules and technologies are applied in the mobile application, allowing users to access various experiment test rigs in the remote laboratory with intuitive experimental immersion. Moreover, a complete laboratory management system to manage the massive users from different universities has also been integrated into the mobile application. At the end, a remote networked control experiment has been conducted to prove the practicability of the mobile application.

Source of combinatorial innovation: functional modules in a product

Source of combinatorial innovation: functional modules in a product

Robust Control of Dielectric Elastomer Smart Actuator for Tracking High-Frequency Trajectory

Dielectric elastomer smart actuator (DESA) has shown great potentials in soft robot applications. The control of the DESA is one of key issues for soft robots. However, the precise control of the DESA is a challenge work, especially when tracking high-frequency trajectories and guaranteeing a certain robustness. To this end, this paper presents a robust tracking control method for a DESA to track various high-frequency trajectories. Firstly, to characterize the complex nonlinear properties (including the quadratic input, asymmetric and rate-dependent hysteresis, as well as creep) of the DESA, its dynamic model is built by combining a square function module, a modified Prandtl-Ishlinskii (P-I) hysteresis model and a linear system. Subsequently, by combining the inverse of the modified P-I hysteresis model with a square root function module, the quadratic input and asymmetric hysteresis properties of the DESA are compensated to reduce tracking the control error. Since model uncertainties and external disturbances are unavoidable in practical applications, a robust controller is designed to achieve the tracking control of the DESA, the controller also guarantees the robustness of the control system. In the end, the effectiveness of the proposed control method is demonstrated through a series of tracking control experiments with different high-frequency trajectories, whose maximal frequency is 8 (Hz). The root- mean-square errors of all experimental results are lower than 1.5%, which proves that the presented method is remarkable from the respective of the practical application.

Development of a Korean offshore wind power HSE risk assessment module based on systems engineering approach

This study provides an in-depth comparison of various risk assessment models widely used in modern industry and proposes the most suitable models for offshore wind power risk assessment, focusing on the characteristics and requirements of the offshore wind power industry. The selection of the assessment models is based on various factors, such as the purpose of risk assessment, the stakeholder requirements, and the characteristics of offshore wind power. This study emphasizes the importance of using a combination of different risk assessment models in situations of high uncertainty. Using model-based systems engineering, risk assessment module is designed and implemented with traceability from requirements to detailed functionality. The risk assessment module supports organic conversion between risk assessment models to assess the risks of offshore wind power generation. The functionality and usability of the offshore wind risk assessment module were verified through the evaluation and the field test.

Setup and Applications of Modular Protein Expression Toolboxes (MoPET) for Mammalian Systems.

The design and generation of an optimal protein expression construct is the first and essential step in the characterization of any protein of interest. However, the exchange and modification of the coding and/or noncoding elements to analyze their effect on protein function or generating the optimal result can be a tedious and time-consuming process using standard molecular biology cloning methods. To streamline the process to generate defined expression constructs or libraries of otherwise difficult to express proteins, the Modular Protein Expression Toolbox (MoPET) has been developed (Weber E, PloS One 12(5):e0176314, 2017). The system applies Golden Gate cloning as an assembly method and follows the standardized modular cloning (MoClo) principle (Weber E, PloS One 6(2):e16765, 2011). This cloning platform allows highly efficient DNA assembly of pre-defined, standardized functional DNA modules effecting protein expression with a focus on minimizing the cloning burden in coding regions. The original MoPET system consists of 53 defined DNA modules divided into eight functional main classes and can be flexibly expanded dependent on the need of the experimenter and expression host. However, already with a limited set of only 53 modules, 792,000 different constructs can be rationally designed or used to generate combinatorial expression optimization libraries. We provide here a detailed protocol for the (1) design and generation of level 0 basic parts, (2) generation of defined expressions constructs, and (3) generation of combinatorial expression libraries.

IMetaTown: A Metaverse System with Multiple Interactive Functions Based on Virtual Reality.

This work aims to pioneer the development of a real-time interactive and immersive Metaverse Human-Computer Interaction (HCI) system leveraging Virtual Reality (VR). The system incorporates a three-dimensional (3D) face reconstruction method, grounded in weakly supervised learning, to enhance player-player interactions within the Metaverse. The proposed method, two-dimensional (2D) face images, are effectively employed in a 2D Self-Supervised Learning (2DASL) approach, significantly optimizing 3D model learning outcomes and improving the quality of 3D face alignment in HCI systems. The work outlines the functional modules of the system, encompassing user interactions such as hugs and handshakes and communication through voice and text via blockchain. Solutions for managing multiple simultaneous online users are presented. Performance evaluation of the HCI system in a 3D reconstruction scene indicates that the 2DASL face reconstruction method achieves noteworthy results, enhancing the system's interaction capabilities by aiding 3D face modeling through 2D face images. The experimental system achieves a maximum processing speed of 18 frames of image data on a personal computer, meeting real-time processing requirements. User feedback regarding social acceptance, action interaction usability, emotions, and satisfaction with the VR interactive system reveals consistently high scores. The designed VR HCI system exhibits outstanding performance across diverse applications.

Suaeda salsa in Relation to Hydrological Connectivity: From the View of Plant Trait Networks

How plant traits respond to environment changes has been given more concerns worldwide. However, it is hard to reveal the integrative responses of plants only based on independent plant traits without considering the close links among plant traits. Plant trait network (PTN) is emerging as a new way to study how plant traits adapt to changing environment and to find out the key plant trait. We collected soil and plant samples from five sampling zones in Suaeda salsa wetlands of the Yellow River Delta in China to construct hydrological connectivity index (HCI) by principal component analysis of eight indicators. PTNs were estimated by network analysis of nine plant traits. The results showed that five study areas had significant different HCIs. The PTNs showed the max tightness in areas with medium HCI and the complexity of PTNs decreased with the rise of HCI. Generally, PTNs exhibited the best performance in the areas with medium HCI in which were the most appropriate for plants to grow. Plant aboveground biomass was the central trait PTNs since it had a high degree as well as betweenness centrality. The findings indicate that Suaeda salsa takes different growth strategies under different hydrological connectivity conditions. Suaeda salsa enhanced the connections of different traits in areas which were the best for plants to grow while Suaeda salsa formed different groups of function modules in areas where hydrological connectivity was weak. This study may give new sights on how plant response to the change of hydrological connectivity.

Research on the Application of Computer Virtual Reality Technology in Tourism Development of Scenic Spots

VR (Virtual Reality) makes it possible to use digital technology to drive the development and revitalization of the cultural tourism industry. Aiming at the shortcomings of traditional virtual tourism system such as weak immersion and lack of interaction, this paper designs and produces a Zhuhai virtual tourism system based on RPG. Secondly, this paper analyzes the functional modules of the virtual tourist attraction system. Finally, this paper uses three-dimensional panoramic technology to realize the development of virtual tourist attractions system from four processes: material collection, material stitching, panoramic roaming, and publishing roaming. The system constructs a virtual three-dimensional tourism environment through virtual reality non-contact sensing technology on the basis of the real tourism landscape. Tourists can experience travel in a virtual environment in advance without leaving home, and then plan efficient travel plans.

Conception on Public Health Surveillance System Based on Big Data in Health and Medical Care

In recent years, public health events in varying degrees have occurred in different parts of China, these public health emergencies are characterized by volatility and complexity. To effectively respond to major challenges, we must establish a comprehensive public health surveillance system capable of efficiently collecting, analyzing, and processing data, while providing effective and timely information support. This paper focuses on how big data technologies can be used to achieve this goal. The theoretical basis is first introduced, including the definition, characteristics and application methods of big data; then the existing public health surveillance system is analyzed and summarized, its shortcomings are pointed out; finally, a design scheme for a public health monitoring system based on health care big data is proposed, detailing the design ideas, functional modules and database structure of the scheme. The scheme can help the authorities to monitor and manage information in real time so as to provide the basis for scientific-decision making. Through testing and verification, the scheme is found to have good performance and can meet the needs of large-scale public health surveillance. The findings of this project hold significant relevance in advancing the development of the public health surveillance system in China.