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Digital twin (DT) technology facilitates the integration of physical and virtual worlds, enabling optical networks to realize efficient, intelligent, and accurate monitoring, prediction, and maintenance. However, the combination of DT and optical networks creates new security challenges on network management. Accurate and complete modeling, decision-making, and feedback of DT optical networks (DTONs) are unattainable without ensuring the reliability and integrity of data sources, collected data, models, and feedback instructions. To address these problems, a blockchain-based DTON architecture is designed, along with a blockchain-based solution to ensure the security and credibility of data sources, and the integrity and immutability of collected data, models, and instructions. The chaincode was developed to implement the functions of blockchain-based DTON, including device registration, mirror and functional model registration, data storage, and decision-making delivery. Furthermore, the proposed solution is deployed using Hyperledger Fabric, and validated with multiple examples, such as routing computation, quality of transmission (QoT) estimation and link optimization, and failure prediction. Hyperledger Caliper was used for determining the effects of send rate, database, number of organizations on the performance, and resource consumption. The experimental results have demonstrated improvements in network performance, model accuracy, and service quality with the implementation of blockchain-based DTON.

The optical network encompasses numerous devices and links, generating a significant volume of logs. Analyzing these logs is significant for network optimization, failure diagnosis, and health monitoring. However, the large-scale and diverse formats of optical network logs present several challenges, including the high cost and difficulty of manual processing, insufficient semantic understanding in existing analysis methods, and the strict requirements for data security and privacy. Generative artificial intelligence (GAI) with powerful language understanding and generation capabilities has the potential to address these challenges. Large language models (LLMs) as a concrete realization of GAI are well-suited for analyzing DCI logs, replacing human experts and enhancing accuracy. Additionally, LLMs enable intelligent interactions with network administrators, automating tasks and improving operational efficiency. Moreover, fine-tuning with open-source LLMs protects data privacy and enhances log analysis accuracy. Therefore, we introduce LLMs and propose a log analysis method with instruction tuning using LLaMA2 for log parsing, anomaly detection and classification, anomaly analysis, and report generation. Real log data extracted from the field-deployed network was used to design and construct instruction tuning datasets. We utilized the dataset for instruction tuning and demonstrated and evaluated the effectiveness of the proposed scheme. The results indicate that this scheme improves the performance of log analysis tasks, especially a 14% improvement in exact match rate for log parsing, a 13% improvement in F1-score for anomaly detection and classification, and a 23% improvement in usability for anomaly analysis, compared with the best baselines.

The proliferating development of optical networks has broadened the network scope and caused a corresponding rise in equipment deployment. This growth potentially results in a significant number of alarms in the case of equipment malfunctions or broken fiber. Managing these alarms efficiently and accurately has always been a critical concern within the research and industry community. The alarm processing workflow typically includes filtration, analysis, and diagnostic stages. In current optical networks, these procedures are often performed by experienced engineers, utilizing their expert knowledge and extensive experience. This method requires considerable human resources and time, as well as demanding proficiency prerequisites. To address this issue, we propose an intelligent alarm analysis assistant, “AlarmGPT,” for optical networks, utilizing a generative pre-trained transformer (GPT) and LangChain. The proposed AlarmGPT exhibits a high level of semantic comprehension and contextual awareness of alarm data, significantly enhancing the model’s ability of interpreting, classifying, and solving alarm events. Through verification of extensive alarm data collected from real optical transport networks (OTNs), the usability of AlarmGPT has been validated in the tasks of alarm knowledge Q&A, alarm compression, alarm priority analysis, and alarm diagnosis. This method has the potential to significantly reduce the labor and time required for alarm processing, while also lowering the experiential requisites incumbent upon network operators.

In recent years, optical networks have become increasingly large-scale and complex, resulting in mounting pressures on operation and maintenance management. The techniques of the artificial intelligence (AI)-driven digital twin (DT) have been applied to optical networks to pave the way for creating the self-optimizing and self-controlling autonomous optical network (AON). However, the AON is confronted with many security and reliability issues: the centralized architecture is prone to data loss during system disconnection and active/standby switchover, and the massive data, DT models, and control instructions in the AON all face the risk of being tampered with, directly affecting the effectiveness of network regulation. In addition, the reliability of complex autonomous processes should be ensured. To address these challenges, in this paper, we applied blockchain to a DT-driven AON with the designed architecture and deployment scheme to realize data loss prevention, anti-tamper storage, secure autonomous operations, access control, and network element controller decisions. A blockchain-based solution was studied by considering the quality of transmission (QoT) estimation and link optimization as examples. Four chaincodes were designed and developed to perform the model storage, service definition, telemetry subscription, data collection, model implementation, and instruction delivery in the AON. Furthermore, the proposed solution was developed and deployed using Hyperledger Fabric. The Hyperledger Caliper was used to test the performance of the proposed blockchain system. The distributed environment is used for performance testing to simulate the real scenario. The test results are analyzed to indicate the future development direction.

Phthalate esters (PAEs) and bisphenol A (BPA) are endocrine-disrupting chemicals (EDCs), which are widely used in the production of food plastic packaging and easily migrate to food. Continuous exposure to EDCs may cause harm to human health. Herein, magnetic covalent organic framework TFP-NDA/Fe3O4was synthesized by magnetizing covalent organic framework TFP-NDA through a facile coprecipitation method, and used as an adsorbent for rapid solid-phase extraction of PAEs (diethyl phthalate (DEP), diisobutyl phthalate (DIBP) and dibutyl phthalate (DBP)) and BPA. The extraction equilibrium can be reached within 12 min. By combination with a gas chromatography-flame ionization detector, the limits of detection were 0.7–2.3 μg L−1and the linear ranges were 10–500 μg L−1for diethyl phthalate (DEP) and 10–1000 μg L−1for diisobutyl phthalate (DIBP), dibutyl phthalate (DBP) and BPA with R2> 0.9916. In beverage samples (plastic bottled drinking water, juice and carbonated drink), the developed method was successfully applied to extract and quantify PAEs and BPA with recoveries ranging from 81.7% to 114.2%.

Herein, we report a hydrodefluorination reaction of polyfluoroarenes catalyzed by bismuthinidenes, Phebox-Bi(I) and OMe-Phebox-Bi(I). Mechanistic studies on the elementary steps support a Bi(I)/Bi(III) redox cycle that comprises C(sp2)–F oxidative addition, F/H ligand metathesis, and C(sp2)–H reductive elimination. Isolation and characterization of a cationic Phebox-Bi(III)(4-tetrafluoropyridyl) triflate manifests the feasible oxidative addition of Phebox-Bi(I) into the C(sp2)–F bond. Spectroscopic evidence was provided for the formation of a transient Phebox-Bi(III)(4-tetrafluoropyridyl) hydride during catalysis, which decomposes at low temperature to afford the corresponding C(sp2)–H bond while regenerating the propagating Phebox-Bi(I). This protocol represents a distinct catalytic example where a main-group center performs three elementary organometallic steps in a low-valent redox manifold.

Herein we present the catalytic activation of N2O at a BiI⇄BiIIIredox platform. The activation of such a kinetically inert molecule was achieved by the use of bismuthinidene catalysts, aided by HBpin as reducing agent. The protocol features remarkably mild conditions (25 °C, 1 bar N2O), together with high turnover numbers (TON, up to 6700) and turnover frequencies (TOF). Analysis of the elementary steps enabled structural characterization of catalytically relevant intermediates after O-insertion, namely a rare arylbismuth oxo dimer and a unique monomeric arylbismuth hydroxide. This protocol represents a distinctive example of a main-group redox cycling for the catalytic activation of N2O.

Two organic–inorganic hybrids, [3-H2DPNDI][ZnCl4]·0.5H2O (1) and [4-H2DPNDI][ZnCl4]·0.5H2O (2) (3-H2DPNDI2+= protonated N,N-di(3-pyridyl)-1,4,5,8-naphthalene diimide, 4-H2DPNDI2+= protonated N,N-di(4-pyridyl)-1,4,5,8-naphthalene diimide), have been designed and prepared. Compounds 1and 2are constructed from typical [ZnCl4]2–inorganic clusters and positional isomeric organic cations of [H2DPNDI]2+, which are assembled into different three-dimensional supramolecular networks via different anion-π interactions and hydrogen-bonding interactions. Photochromism of 1and non-photochromism of 2are attributable to the usage of positional isomers, which leads to the different spatial stack of [ZnCl4]2–donors and [H2DPNDI]2+acceptors, suggesting a delicate modulating effect of the distinct interfacial contacts on the intermolecular charge transfer/electron transfer and consequently photoresponsive behaviors.

Bisphenols (BPs) pose a pervasive threat to human health due to their potential to leach from plastic packaging into food, thereby endangering the endocrine system. Given their low concentrations in complex samples, the development of a swift, efficient pretreatment method is imperative. In this study, a magnetic covalent organic framework (Fe3O4@TzDa) was synthesized via a layer-by-layer growth approach, utilized in rapid magnetic solid-phase extraction (MSPE) combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the determination BPs. This method exhibited remarkable sensitivity, detecting five BPs (BPS, BPF, BPA, BPB, and BPAF) at levels between 0.68 and 0.77 μg L-1. Fe3O4@TzDa nanocomposites maintained an extraction efficiency of approximately 98.0% over five cycles, ensuring robust and repeatable performance. Sample analysis, encompassing plastic bottled water, pear juice, mix juices, pop-top canned curry beef, and box-packed milk, yielded recoveries of 72.0% to 114.0% (RSDs < 7.5%, n= 5). Furthermore, BPs migration in sports cups and portable bottles was evaluated, revealing migration levels ranging from 0.0014 to 0.0058 mg kg−1. This study advances BP analysis by introducing a reliable method for their efficient determination in beverage samples from plastic food packages. The utilization of Fe3O4@TzDa underscores a pivotal advancement in sample preparation, enhancing both sensitivity and efficiency for the extraction and determination of BPs in food samples.

The widespread use of antibiotics leads to spread of resistance bacteria and genes in natural water and has a potential threat to human health. Herein, a nanorod CoNiFe-MOF/LDH was synthesized using a self-sacrificial strategy and employed to degrade sulfamethazine (SMT) by heterogeneous electro-Fenton (HEF). The conversion of CoNiFe-LDH to CoNiFe-MOF/LDH resulted in a significant enhancement of specific surface area (from 13.96 to 32.26 m2g-1) and electroactive surface area (from 2.20 to 4.22 mm2), and the degradation rate of SMT increased from 88.53% to 98.41% within 60min. The free radical capture experiments indicated that ·OH and 1O2played a significant role in the HEF process. The CoNiFe-MOF/LDH catalyst could be recycled 5 times with degradation rate of over 90%, it also demonstrated excellent resistance towards inorganic ions and humic acid. In addition, the CoNiFe-MOF/LDH exhibited remarkable degradation efficiency for 8 sulfonamides (over 89%). Mass spectrometry analysis indicated that SMT was oxidatively degraded via three possible pathways. According to the analysis of Toxicity Estimation Software Tool, the acute toxicity of SMT was effectively weakened. The CoNiFe-MOF/LDH catalyst has potential application prospect in wastewater treatment.

Although transition-metal-catalyzed B–H bond insertion of carbenes into stable borane adducts has emerged as a promising method for organoborane synthesis, all the diazo compounds used to date as carbene precursors have had an electron-withdrawing group to stabilize them. Herein, we report a protocol for rhodium-catalyzed B–H bond insertion reactions of unstabilized diazo compounds generated in situfrom tosylhydrazones. In addition, by using chiral dirhodium catalysts, we also achieved an asymmetric version of the reaction with good to excellent enantioselectivities (up to 98:2 e.r.). This is the first enantioselective heteroatom–hydrogen bond insertion reaction to use unstabilized diazo compounds as carbene precursors. The protocol exhibited good functional group tolerance and could be carried out on a gram scale. It also enabled one-pot transformation of a carbonyl group to a boryl group enantioselectively. The B–H bond insertion products could be easily transformed into chiral alcohols and other widely used organoboron reagents with enantiomeric fidelity.

In this paper, experimental and numerical analyses were conducted to investigate the structural properties and failure modes of the blind bolted flush or extended end plate joints between concrete-filled steel tubular (CFST) columns and composite beams. The experimental program and the main results of the tests were elaborated. Four full-scale subassemblages beam-tocolumn exterior joints to hot-rolled and cold-formed CFST columns were tested. The failure modes and moment-rotation relation curves of the joints were evaluated. The test results showed that all specimens exhibited favorable strength and stiffness, and larger deformation capacities. Meanwhile, finite element analysis (FEA) modeling of the specimens was built and analyzed. The results obtained from the FEA modeling were satisfactory agreement with the experimental tests results. Then, an extensive parametric study was conducted to investigate the influence of various parameters on the moment capacity and rotation stiffness of composite joints under positive or negative moment. The experimental studies and numerical analyses can improve the design of the typed joints to be used in moment-resisting composite frames.

Large-scale sheep farming has conventionally relied on barcodes and ear tags, devices that can be difficult to implement and maintain, for sheep identification and tracking. Biological data have also been used for tracking in recent years but have not been widely adopted due to the difficulty and high costs of data collection. To address these issues, a noncontact facial recognition technique is proposed in this study, in which training data were acquired in natural conditions using a series of video cameras, as Dupo sheep walked freely through a gate. A key frame extraction algorithm was then applied to automatically generate sheep face data sets representing various poses. An improved MobilenetV2 framework, termed Order-MobilenetV2 (O-MobilenetV2), was developed from an existing advanced convolutional neural network and used to improve the performance of feature extraction. In addition, O-MobilenetV2 includes a unique conv3x3 deep convolution module, which facilitated higher accuracy while reducing the number of required calculations by approximately two-thirds. A series of validation tests were performed in which the algorithm identified individual sheep using facial features, with the proposed model achieving the highest accuracy (95.88%) among comparable algorithms. In addition to high accuracy and low processing times, this approach does not require significant data pre-processing, which is common among other models and prohibitive for large sheep populations. This combination of simple operation, low equipment costs, and high robustness to variable sheep postures and environmental conditions makes our proposed technique a viable new strategy for sheep facial recognition and tracking.

Based on the traditional DSR Protocol has obvious drawbacks such as short network lifetime in the small reconnaissance UAV ad hoc network, a routing protocols called UEDSR(UAV Energy Dynamic Source Routing Protocol) suitable for small reconnaissance UAV ad hoc network is proposed. This protocol introduces the energy balancing mechanism into the algorithms based on DSR, then improves the situation of excessive energy consumption of hot nodes and prolongs the network lifetime. The simulation of protocol by NS2 illustrates that comparing to DSR the UEDSR protocol achieves about 7% increase in the network lifetime and the packet delivery and the average of network throughput are effectively improved.

The central control system, as a very important device of computer-assisted instruction, has been applied generally in all different level of schools. However, due to all kinds of reasons, teachers and teaching administrators have long experienced the inconvenience on operating the projector and screen. The main device of common CAI system consists of computer, projector and screen which can actually solve such main problems such as the projector control and the lift control of screen. Since the projector is operated most often via the remote controller in the school, and under the circumstance that teachers familiarize the equipment operation by different levels, the operation and application can be incredibly inconvenient. The central control system aims to promote the control of CAI System up to a new level in the way that changes the operating methods of operators and simplifies the operating process. For the reasons and situations listed above, a high-efficient, simplified and low-cost management and application of teaching equipment is needed, and as a result of it, we develop and design the Central Control System for Multimedia Instruction Window.

In this paper, a novel mobile robot system for searching books automatically in libraries is designed based on the radio frequency identification (RFID) and singlechip programming techniques. Recently, the barcode identification is frequently used in libraries. However, because of its unique advantages of RFID technology and its application in the intelligent robots will be able to achieve high-efficient automatic search of books. RFID has the advantages of large information storage and long identification distance etc., thus application in library management information system has great development potential. By using RFID long-distance identification ability combined with the close accurate positioning technologies, a mobile robot for automatic search of books in libraries is designed, which can effectively improve the book search efficiency, saves the massive cost and improve the library informatization level.

This paper proposes an obstacle avoidance scheme of a novel mobile robot system for automatic search of books in libraries based on the radio frequency identification (RFID). Nowadays, most of the libraries utilize the barcode identification technique, since it is of low cost. However, our experiments show that using RFID adequately can save more labor and improve the library management efficiency, since RFID has many advantages, i.e., large information storage and long identification distance, etc., whose application in library management information system has great development potential. Based the advantages of its long-distance identification ability combined with the close accurate positioning technologies, an intelligent mobile robot system for automatic search of books anywhere in libraries is design in our project, which can effectively improve the book search efficiency, saves the massive cost and improve the library informatization level. According to this mobile robot, we give a fuzzy multisensor fusion technique based obstacle avoidance method, which can make the intelligent robot searching the desired books automatically in libraries. Both the simulation and the actual experiments validate the obstacle-avoiding algorithm.

This paper, based on the method of Applied statistics and experimental psychology, use the principal component analysis method to Quantitative research the user's perceptual knowledge of household car shape design, and try to establish user's mental models of product form characteristics. Draw the image of automobile products distribution, so as to find the car of product form characteristics of the blank image area, for the user the value orientation of perceptual consciousness provides the possibility of numerical quantitative way.

Lagrange-Maxwell equations and Park transform are adopted based on electrical and mechanical energy equations consisting of servomotor parameters. Lagrange-Maxwell equations are transformed from three-phase stator reference coordinates to two-phase rotor reference coordinates. Electromechanical coupling dynamics equations of permanent magnet synchronous servomotor in two-phase reference coordinates are obtained. In this dynamical modeling method of electromechanical coupling system, to establish dynamical differential equations needs to measure amplitude of the flux induced by the permanent magnets and the winding's inductance in dq0 reference of the motor but needs not to measure the size of magnetic circuit. The equations deduced is terse, efficient, and the equations easy to use. Electromechanical coupling dynamics system, servomotor is simulated. Simulation results show that the electromechanical coupling dynamics equations deduced for servomotor are correct, and current control schemes are reasonable.

To achieve high precision contour tracking control for Permanent Magnet Linear Synchronous motor drive X-Y table, genetic algorithm was used on PID controller parameters optimized. By the ability of gene mutation, the parameters were avoided falling into local optimum point. Consequently, the global optimal value can be found directly. Variable-gain cross coupling controller was used to compensate X-Y axis coupling error. Eventually the path tracking contour error is greatly reduced to the expected accuracy limits. Theoretical analysis and simulation results show that the designed control system make X-Y table possess high contour accuracy

Staphylococcus aureus(S. aureus) is one of the most common food-borne pathogens, and its rapid detection is important for food safety. In this paper, a method for rapid detection of S. aureusby using cyclic strand displacement polymerase reaction (CSDPR) for turn-on molecular beacons (MBs) was proposed. The highly conserved 16S rDNA fragment sequence of S. aureuswas screened as the target nucleic acid, and was detected by CSDPR amplification. Under the optimized condition, the linear range of the method was 2.0 × 105–2.5 × 106 CFU/mL, and the detection limit was 9.5 × 104 CFU/mL. The reaction time of CSDPR was 15 min. This method has been applied to the rapid detection of S. aureusin spiked milk and steak samples and the detection results are consistent with those of plate counting method.