Polyethylene glycol fusion repair of severed rat sciatic nerves reestablishes axonal continuity and reorganizes sensory terminal fields in the spinal cord

Peripheral nerve injuries result in the rapid degeneration of distal nerve segments and immediate loss of motor and sensory functions;behavioral recovery is typically poor.We used a plasmalemmal fusogen,polyethylene glycol(PEG),to immediately fuse closely apposed open ends of severed proximal and distal axons in rat sciatic nerves.We have previously reported that sciatic nerve axons repaired by PEG-fusion do not undergo Wallerian degeneration,and PEG-fused animals exhibit rapid(within 2–6 weeks)and extensive locomotor recovery.Furthermore,our previous report showed that PEG-fusion of severed sciatic motor axons was non-specific,i.e.,spinal motoneurons in PEG-fused animals were found to project to appropriate as well as inappropriate target muscles.In this study,we examined the consequences of PEG-fusion for sensory axons of the sciatic nerve.Young adult male and female rats(Sprague–Dawley)received either a unilateral single cut or ablation injury to the sciatic nerve and subsequent repair with or without(Negative Control)the application of PEG.Compound action potentials recorded immediately after PEG-fusion repair confirmed conduction across the injury site.The success of PEG-fusion was confirmed through Sciatic Functional Index testing with PEG-fused animals showing improvement in locomotor function beginning at 35 days postoperatively.At 2–42 days postoperatively,we anterogradely labeled sensory afferents from the dorsal aspect of the hindpaw following bilateral intradermal injection of wheat germ agglutinin conjugated horseradish peroxidase.PEG-fusion repair reestablished axonal continuity.Compared to unoperated animals,labeled sensory afferents ipsilateral to the injury in PEG-fused animals were found in the appropriate area of the dorsal horn,as well as inappropriate mediolateral and rostrocaudal areas.Unexpectedly,despite having intact peripheral nerves,similar reorganizations of labeled sensory afferents were also observed contralateral to the injury and repair.This central reorganization may contribute to the improved behavioral recovery seen after PEG-fusion repair,supporting the use of this novel repair methodology over currently available treatments.

Controlling chaos based on a novel intelligent integral terminal sliding mode control in a rod-type plasma torch

An integral terminal sliding mode controller is proposed in order to control chaos in a rod-type plasma torch system.In this method, a new sliding surface is defined based on a combination of the conventional sliding surface in terminal sliding mode control and a nonlinear function of the integral of the system states. It is assumed that the dynamics of a chaotic system are unknown and also the system is exposed to disturbance and unstructured uncertainty. To achieve a chattering-free and high-speed response for such an unknown system, an adaptive neuro-fuzzy inference system is utilized in the next step to approximate the unknown part of the nonlinear dynamics. Then, the proposed integral terminal sliding mode controller stabilizes the approximated system based on Lyapunov＇s stability theory. In addition, a Bee algorithm is used to select the coefficients of integral terminal sliding mode controller to improve the performance of the proposed method. Simulation results demonstrate the improvement in the response speed, chattering rejection, transient response,and robustness against uncertainties.

Real- Time Color Enhancement Method Used for Intelligent Mobile Terminals

In certain environments and under some conditions, the video images taken by the intelligent mobile video phones seem dark, and the colors are not bright or saturated enough.This paper presents an adaptive method to enhance the video image brightness visualization and the color performance depending on the certain hardware property and function parameters. The experimental results prove that this method can enhance the colors and the contrast of the video images, based on the estimated quality feature values of each frame, without using the extra Digital Signal Processor (DSP).

Impact of NGN Architecture by Intelligent Terminals

This paper discusses the impacts on the existing NGN network architecture by the intelligent terminals or NGN terminals. An economical and simple network with multitudes of services can be realized if terminal intelligence is utilized for functions traditionally performed by the network. The existing terminals without required intelligence can also be supported via terminal emulation at the edge of the network. Communication is established between the interested parties by the combined efforts of the NGN terminals and the network. A possible network architecture where the NGN terminals and NGN network co-operation is suggested. The benefits for such network, such as increased network robustness, interoperability and scalability, are explored in this paper.

The Intelligent Terminal Design for Integrated Substation Auxiliary System

The integrated substation auxiliary system is a project composed by software and hardware to help operators of a substation carry daily maintain works. The intelligent terminal is the key part of the system and it provides important UI and communication functions for the user. Smart phones can achieve higher bandwidth data communication, and at the same time can carry Bluetooth communication, video call, multimedia display and other functions. Smart phone is a potential common platform, suitable for the deployment of substation integrated auxiliary system terminal. Based on the Android Smart phone platform, this paper develops intelligent client-side software for the integrated substation auxiliary system and it can realize the operation ticket control, the video call, the substation map display and other functions. This client-side software has been applied in practice, and has gained good effects.

A Discussion on the Practical Teaching Reform of Intelligent Mobile Terminal Technology Course

In this intelligent era,there is a high market demand for professional engineering talents in the computer industry.Based on engineering education accreditation,this paper briefly discusses the practical teaching reform of the intelligent mobile terminal technology course,which involves integrating theory with practical,taking students as the center,focusing on practical teaching,strengthening students’practical skills and computer application development skills,as well as promoting the construction and development of engineering accreditation.

Machine learning for predicting the outcome of terminal ballistics events

Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression models,extreme gradient boosting(XGBoost),artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GP),on two common terminal ballistics’ problems:(a)predicting the V50ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments,and(b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness.To achieve this we utilise two datasets,each consisting of approximately 1000samples,collated from public release sources.We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range.Although extrapolation is not advisable for ML-based regression models,for applications such as lethality/survivability analysis,such capability is required.To circumvent this,we implement expert knowledge and physics-based models via enforced monotonicity,as a Gaussian prior mean,and through a modified loss function.The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models,providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not.The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types,target materials and thicknesses,and impact conditions significantly more diverse than that achievable from any existing analytical approach.Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster.We provide some general guidelines throughout for the development,application,and reporting of ML models in terminal ballistics problems.

Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

Acute suppurative terminal cholangitis:Clinical characteristics of a new subtype of acute cholangitis

Background:Acute suppurative terminal cholangitis(ASTC)is rarer than acute obstructive cholangitis and is not well studied.To explore this subtype of acute cholangitis,we described our clinical experience with ASTC.Methods:We performed a retrospective review of patients with ASTC admitted to our center from September 2014 to August 2020.We analyzed their clinical characteristics,including etiology,clinical manifestations,imaging features,treatment and prognosis.Results:A total of 32 ASTC patients were included in the analysis.The majority of the patients had a history of biliary operations,and clinical manifestations were occult and atypical.The positive rate of bacterial culture was 46.9%.All the patients had typical imaging features on computed tomography and magnetic resonance imaging.Treatment with effective antibiotics was provided as soon as diagnosis was established.After treatment,most patients had a good outcome.Elevated levels of total bilirubin,aspartate aminotransferase,procalcitonin and gamma-glutamyltransferase were the characteristics of critically ill patients and were associated with relatively poor prognosis.Conclusions:Our results demonstrated that ASTC should be recognized as a new subtype of acute cholangitis,and that earlier diagnosis and more personalized treatments are needed.

Cooperative User-Scheduling and Resource Allocation Optimization for Intelligent Reflecting Surface Enhanced LEO Satellite Communication

Lower Earth Orbit(LEO) satellite becomes an important part of complementing terrestrial communication due to its lower orbital altitude and smaller propagation delay than Geostationary satellite. However, the LEO satellite communication system cannot meet the requirements of users when the satellite-terrestrial link is blocked by obstacles. To solve this problem, we introduce Intelligent reflect surface(IRS) for improving the achievable rate of terrestrial users in LEO satellite communication. We investigated joint IRS scheduling, user scheduling, power and bandwidth allocation(JIRPB) optimization algorithm for improving LEO satellite system throughput.The optimization problem of joint user scheduling and resource allocation is formulated as a non-convex optimization problem. To cope with this problem, the nonconvex optimization problem is divided into resource allocation optimization sub-problem and scheduling optimization sub-problem firstly. Second, we optimize the resource allocation sub-problem via alternating direction multiplier method(ADMM) and scheduling sub-problem via Lagrangian dual method repeatedly.Third, we prove that the proposed resource allocation algorithm based ADMM approaches sublinear convergence theoretically. Finally, we demonstrate that the proposed JIRPB optimization algorithm improves the LEO satellite communication system throughput.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

Introduction to the Special Issue on Machine Learning-Guided Intelligent Modeling with Its Industrial Applications

With the advancement of Artificial Intelligence(AI)technology,traditional industrial systems are undergoing an intelligent transformation,bringing together advanced computing,communication and control technologies,Machine Learning(ML)-based intelligentmodelling has become a newparadigm for solving problems in the industrial domain[1–3].With numerous applications and diverse data types in the industrial domain,algorithmic and data-driven ML techniques can intelligently learn potential correlations between complex data and make efficient decisions while reducing human intervention.However,in real-world application scenarios,existing algorithms may have a variety of limitations,such as small data volumes,small detection targets,low efficiency,and algorithmic gaps in specific application domains[4].Therefore,many new algorithms and strategies have been proposed to address the challenges in industrial applications[5–8].

Vision based intelligent traffic light management system using Faster R‐CNN

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Intelligent casting:Empowering the future foundry industry

Emerging technological advances are reshaping the casting sector in latest decades.Casting technology is evolving towards intelligent casting paradigm that involves automation,greenization and intelligentization,which attracts more and more attention from the academic and industry communities.In this paper,the main features of casting technology were briefly summarized and forecasted,and the recent developments of key technologies and the innovative efforts made in promoting intelligent casting process were discussed.Moreover,the technical visions of intelligent casting process were also put forward.The key technologies for intelligent casting process comprise 3D printing technologies,intelligent mold technologies and intelligent process control technologies.In future,the intelligent mold that derived from mold with sensors,control devices and actuators will probably incorporate the Internet of Things,online inspection,embedded simulation,decision-making and control system,and other technologies to form intelligent cyber-physical casting system,which may pave the way to realize intelligent casting.It is promising that the intelligent casting process will eventually achieve the goal of real-time process optimization and full-scale control,with the defects,microstructure,performance,and service life of the fabricated castings can be accurately predicted and tailored.

An intelligent prediction model of epidemic characters based on multi-feature

The epidemic characters of Omicron(e.g.large-scale transmission)are significantly different from the initial variants of COVID-19.The data generated by large-scale transmission is important to predict the trend of epidemic characters.However,the re-sults of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission.In consequence,these inaccurate results have negative impacts on the process of the manufacturing and the service industry,for example,the production of masks and the recovery of the tourism industry.The authors have studied the epidemic characters in two ways,that is,investigation and prediction.First,a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters.Second,theβ-SEIDR model is established,where the population is classified as Susceptible,Exposed,Infected,Dead andβ-Recovered persons,to intelligently predict the epidemic characters of COVID-19.Note thatβ-Recovered persons denote that the Recovered persons may become Sus-ceptible persons with probabilityβ.The simulation results show that the model can accurately predict the epidemic characters.

Edge modes in finite-size systems with different edge terminals

We investigate the behavior of edge modes in the presence of different edge terminations and long-range(LR)hopping.Here,we mainly focus on such model crystals with two different types of structures(type I:“…-P-Q-P-Q-…”and type II:“…=P-Q=P-Q=…”),where P and Q represent crystal lines(CLs),while the symbols“-”and“=”denote the distance between the nearest neighbor(NN)CLs.Based on the lattice model Hamiltonian with LR hopping,the existence of edge modes is determined analytically by using the transfer matrix method(TMM)when different edge terminals are taken into consideration.Our findings are consistent with the numerical results obtained by the exact diagonalization method.We also notice that edge modes can exhibit different behaviors under different edge terminals.Our result is helpful in solving novel edge modes in honeycomb crystalline graphene and transition metal dichalcogenides with different edge terminals.

Intelligent Transformation: General Intelligence Theory

This paper aims to formalize a general definition of intelligence beyond human intelligence. We accomplish this by re-imagining the concept of equality as a fundamental abstraction for relation. We discover that the concept of equality = limits the sensitivity of our mathematics to abstract relationships. We propose a new relation principle that does not rely on the concept of equality but is consistent with existing mathematical abstractions. In essence, this paper proposes a conceptual framework for general interaction and argues that this framework is also an abstraction that satisfies the definition of Intelligence. Hence, we define intelligence as a formalization of generality, represented by the abstraction ∆∞Ο, where each symbol represents the concepts infinitesimal, infinite, and finite respectively. In essence, this paper proposes a General Language Model (GLM), where the abstraction ∆∞Ο represents the foundational relationship of the model. This relation is colloquially termed “The theory of everything”.

The Impact of Privatization on Terminal Efficiency: A Case Study of Tema Port

Port and terminal efficiency are of utmost importance to the container shipping industry due to their significance in enhancing the competitive advantage of ports within a region. Consequently, there have always been notable variations of studies around it. This paper analyzes the impact of privatization on terminal efficiency using the Port of Tema as a Case Study. The main objective of this paper is to analyze the efficiency trends of the public and private terminals in the port over the years. To achieve this objective, DEA-CCR methodology was employed to calculate the annual technical efficiency trends of the private and public terminals using four input variables and three output variables. The main results of the paper indicated that the public and private terminals were efficient for multiple years. However, the efficiency scores over the years demonstrated inconsistency, exhibiting notable fluctuations. The findings of this study will aid policymakers across the region on policies relating to the efficiency and ownership structure of ports and terminals.

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.