Design and optimization of fluid lubricated bearings operated with extreme working performances——a comprehensive review

Fluid lubricated bearings have been widely adopted as support components for high-end equipment in metrology,semiconductor devices,aviation,strategic defense,ultraprecision manufacturing,medical treatment,and power generation.In all these applications,the equipment must deliver extreme working performances such as ultraprecise movement,ultrahigh rotation speed,ultraheavy bearing loads,ultrahigh environmental temperatures,strong radiation resistance,and high vacuum operation,which have challenged the design and optimization of reliable fluid lubricated bearings.Breakthrough of any related bottlenecks will promote the development course of high-end equipment.To promote the advancement of high-end equipment,this paper reviews the design and optimization of fluid lubricated bearings operated at typical extreme working performances,targeting the realization of extreme working performances,current challenges and solutions,underlying deficiencies,and promising developmental directions.This paper can guide the selection of suitable fluid lubricated bearings and optimize their structures to meet their required working performances.

Determinants of Satisfaction at Work and Its Reflections on Performance

This study examines the relationship between job satisfaction and performance,investigating personality traits and satisfaction aspects among employees of a Federal Higher Education Institution.A questionnaire was administered to 658 participants,using structural equation modeling for analysis.Results highlighted that challenging work,neuroticism,and self-esteem significantly influenced overall workplace satisfaction,while general satisfaction,self-efficacy,and lack of attention were key determinants of work performance.This emphasizes the importance for managers to prioritize factors enhancing employee satisfaction,as it positively correlates with job performance.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

Two-Way Neural Network Performance PredictionModel Based onKnowledge Evolution and Individual Similarity

Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,etc.Compared with online courses such asMOOCs,students’academicrelateddata in the face-to-face physical teaching environment is usually sparsity,and the sample size is relativelysmall.It makes building models to predict students’performance accurately in such an environment even morechallenging.This paper proposes a Two-WayNeuralNetwork(TWNN)model based on the bidirectional recurrentneural network and graph neural network to predict students’next semester’s course performance using only theirprevious course achievements.Extensive experiments on a real dataset show that our model performs better thanthe baselines in many indicators.

Tree-Based Solution Frameworks for Predicting Tunnel Boring Machine Performance Using Rock Mass and Material Properties

Tunnel Boring Machines(TBMs)are vital for tunnel and underground construction due to their high safety and efficiency.Accurately predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing costs.This study investigates the effectiveness of tree-based machine learning models,including Random Forest,Extremely Randomized Trees,Adaptive Boosting Machine,Gradient Boosting Machine,Extreme Gradient Boosting Machine(XGBoost),Light Gradient Boosting Machine,and CatBoost,in predicting the Penetration Rate(PR)of TBMs by considering rock mass and material characteristics.These techniques are able to provide a good relationship between input(s)and output parameters;hence,obtaining a high level of accuracy.To do that,a comprehensive database comprising various rock mass and material parameters,including Rock Mass Rating,Brazilian Tensile Strength,and Weathering Zone,was utilized for model development.The practical application of these models was assessed with a new dataset representing diverse rock mass and material properties.To evaluate model performance,ranking systems and Taylor diagrams were employed.CatBoost emerged as the most accurate model during training and testing,with R2 scores of 0.927 and 0.861,respectively.However,during validation,XGBoost demonstrated superior performance with an R2 of 0.713.Despite these variations,all tree-based models showed promising accuracy in predicting TBM performance,providing valuable insights for similar projects in the future.

Comparative Performance Measurement of the Pareto Optimal Combination and Multi-Objective Combination Models for Controller Placement in Software-Defined Networks

The evolution of the current network has challenges of programmability, maintainability and manageability, due to network ossification. This challenge led to the concept of software-defined networking (SDN), to decouple the control system from the infrastructure plane caused by ossification. The innovation created a problem with controller placement. That is how to effectively place controllers within a network topology to manage the network of data plane devices from the control plane. The study was designed to empirically evaluate and compare the functionalities of two controller placement algorithms: the POCO and MOCO. The methodology adopted in the study is the explorative and comparative investigation techniques. The study evaluated the performances of the Pareto optimal combination (POCO) and multi-objective combination (MOCO) algorithms in relation to calibrated positions of the controller within a software-defined network. The network environment and measurement metrics were held constant for both the POCO and MOCO models during the evaluation. The strengths and weaknesses of the POCO and MOCO models were justified. The results showed that the latencies of the two algorithms in relation to the GoodNet network are 3100 ms and 2500 ms for POCO and MOCO respectively. In Switch to Controller Average Case latency, the performance gives 2598 ms and 2769 ms for POCO and MOCO respectively. In Worst Case Switch to Controller latency, the performance shows 2776 ms and 2987 ms for POCO and MOCO respectively. The latencies of the two algorithms evaluated in relation to the Savvis network, compared as follows: 2912 ms and 2784 ms for POCO and MOCO respectively in Switch to Controller Average Case latency, 3129 ms and 3017 ms for POCO and MOCO respectively in Worst Case Switch to Controller latency, 2789 ms and 2693 ms for POCO and MOCO respectively in Average Case Controller to Controller latency, and 2873 ms and 2756 ms for POCO and MOCO in Worst Case Switch to Controller latency respectively. The latencies of the two algorithms evaluated in relation to the AARNet, network compared as follows: 2473 ms and 2129 ms for POCO and MOCO respectively, in Switch to Controller Average Case latency, 2198 ms and 2268 ms for POCO and MOCO respectively, in Worst Case Switch to Controller latency, 2598 ms and 2471 ms for POCO and MOCO respectively, in Average Case Controller to Controller latency, 2689 ms and 2814 ms for POCO and MOCO respectively Worst Case Controller to Controller latency. The Average Case and Worst-Case latencies for Switch to Controller and Controller to Controller are minimal, and favourable to the POCO model as against the MOCO model when evaluated in the Goodnet, Savvis, and the Aanet networks. This simply indicates that the POCO model has a speed advantage as against the MOCO model, which appears to be more resilient than the POCO model.

Performance of Gas-Steam Combined Cycle Cogeneration Units Influenced by Heating Network Terminal Steam Parameters

The determination of source-side extracted heating parameters is of great significance to the economic operation of cogeneration systems.This paper investigated the coupling performance of a cogeneration heating and power system multidimensionally based on the operating characteristics of the cogeneration units,the hydraulic and thermodynamic characteristics of the heating network,and the energy loads.Taking a steam network supported by a gas-steam combined cycle cogeneration system as the research case,the interaction effect among the source-side prime movers,the heating networks,and the terminal demand thermal parameters were investigated based on the designed values,the plant testing data,and the validated simulation.The operating maps of the gas-steam combined cycle cogeneration units were obtained using THERMOFLEX,and the minimum source-side steam parameters of the steam network were solved using an inverse solution procedure based on the hydro-thermodynamic coupling model.The cogeneration operating maps indicate that the available operating domain considerably narrows with the rise of the extraction steam pressure and flow rate.The heating network inverse solution demonstrates that the source-side steam pressure and temperature can be optimized from the originally designed 1.11 MPa and 238.8°C to 1.074 MPa and 191.15°C,respectively.Under the operating strategy with the minimum source-side heating parameters,the power peak regulation depth remarkably increases to 18.30%whereas the comprehensive thermal efficiency decreases.The operation under the minimum source-side heating steam parameters can be superior to the originally designed one in the economy at a higher price of the heating steam.At a fuel price of$0.38/kg and the power to fuel price of 0.18 kg/(kW·h),the critical price ratio of heating steam to fuel is 119.1 kg/t.The influence of the power-fuel price ratio on the economic deviation appears relatively weak.

Return to work in young and middle-aged colorectal cancer survivors:Factors influencing self-efficacy,fear,resilience,and financial toxicity

BACKGROUND Return to work(RTW)serves as an indication for young and middle-aged colorectal cancer(CRC)survivors to resume their normal social lives.However,these survivors encounter significant challenges during their RTW process.Hence,scientific research is necessary to explore the barriers and facilitating factors of returning to work for young and middle-aged CRC survivors.AIM To examine the current RTW status among young and middle-aged CRC survivors and to analyze the impact of RTW self-efficacy(RTW-SE),fear of progression(FoP),eHealth literacy(eHL),family resilience(FR),and financial toxicity(FT)on their RTW outcomes.METHODS A cross-sectional investigation was adopted in this study.From September 2022 to February 2023,a total of 209 participants were recruited through a convenience sampling method from the gastrointestinal surgery department of a class A tertiary hospital in Chongqing.The investigation utilized a general information questionnaire alongside scales assessing RTW-SE,FoP,eHL,FR,and FT.To analyze the factors that influence RTW outcomes among young and middle-aged CRC survivors,Cox regression modeling and Kaplan-Meier survival analysis were used.RESULTS A total of 43.54%of the participants successfully returned to work,with an average RTW time of 100 days.Cox regression univariate analysis revealed that RTW-SE,FoP,eHL,FR,and FT were significantly different between the non-RTW and RTW groups(P<0.05).Furthermore,Cox regression multivariate analysis identified per capita family monthly income,job type,RTW-SE,and FR as independent influencing factors for RTW(P<0.05).CONCLUSION The RTW rate requires further improvement.Elevated levels of RTW-SE and FR were found to significantly increase RTW among young and middle-aged CRC survivors.Health professionals should focus on modifiable factors,such as RTW-SE and FR,to design targeted RTW support programs,thereby facilitating their timely reintegration into mainstream society.

Construction of 3D porous Cu_(1.81)S/nitrogen-doped carbon frameworks for ultrafast and long-cycle life sodium-ion storage

Transition metal sulfides have great potential as anode mterials for sodium-ion batteries(SIBs)due to their high theoretical specific capacities.However,the inferior intrinsic conductivity and large volume variation during sodiation-desodiation processes seriously affect its high-rate and long-cyde performance,unbeneficial for the application as fast-charging and long-cycling SIBs anode.Herein,the three-dimensional porous Cu_(1.81)S/nitrogen-doped carbon frameworks(Cu_(1.81)S/NC)are synthesized by the simple and facile sol-gel and annealing processes,which can accommodate the volumetric expansion of Cu_(1.81)S nanoparticles and accelerate the transmission of ions and electrons during Na^(+)insertion/extraction processes,exhibiting the excellent rate capability(250.6 mA·g^(-1)at 20.0 A·g^(-1))and outstanding cycling stability(70% capacity retention for 6000 cycles at 10.0 A·g^(-1))for SIBs.Moreover,the Na-ion full cells coupled with Na_(3)V_(2)(PO_(4))_(3)/C cathode also demonstrate the satisfactory reversible specific capacity of 330.5 mAh·g^(-1)at 5.0 A·g^(-1)and long-cycle performance with the 86.9% capacity retention at 2.0 A·g^(-1)after 750 cycles.This work proposes a promising way for the conversionbased metal sulfides for the applications as fast-charging sodium-ion battery anode.

Multiparameter performance monitoring of pulse amplitude modulation channels using convolutional neural networks

A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.

SIMULATION AND PERFORMANCE ANALYSIS OF NETWORK ON CHIP ARCHITECTURES

The network on chip（NoC）is used as a solution for the communication problems in a complex system on chip（SoC）design.To further enhance performances,the NoC architectures,a high level modeling and an evaluation method based on OPNET are proposed to analyze their performances on different injection rates and traffic patterns.Simulation results for general NoC in terms of the average latency and the throughput are analyzed and used as a guideline to make appropriate choices for a given application.Finally,a MPEG4 decoder is mapped on different NoC architectures.Results prove the effectiveness of the evaluation method.

Performance analysis in 3G/ad hoc integrated network

An analytical approach to evaluate the performance of the 3G/ad hoc integrated network is presented. A channel model capturing both path loss and shadowing is applied to the analysis so as to characterize power falloff vs. distance. The 3G/ad hoc integrated network scenario model is introduced briefly. Based on this model, several performances of the 3G/ ad hoc integrated network in terms of outage probability, call dropping probability and new call blocking probability are evaluated. The corresponding performance formulae are deduced in accordance with the analytical models. Meanwhile, the formula of the 3G/ad hoc integrated network capacity is deduced on the basis of the formula of the outage probability. It is observed from extensive simulation and numerical analysis that the 3G/ad hoc integrated network remarkably outperforms the 3G network with regards to the network performance. This derived evaluation approach can be applied into planning and optimization of the 3G/ad hoc network.

Full-Chip Scalable Routing Framework Considering Congestion and Performance

This paper presents a novel full-chip scalable routing framework that simultaneously considers the routing congestion and the circuit performance. In order to bridge the gap, the presented framework calls the detailed router immediately after a global route is extracted. With the interleaving mode of global routing immediately followed by detailed routing, accurate routing resource and congestion information can be obtained, which provides valuable guidance for the following global routing process. The framework features the fast pattern and framed shortest path global router,a maze-based congestion-driven detailed router, and better interaction between the two routers. In the framework, timing critical nets can be assigned higher priority for performance concern, and different net ordering techniques can be adopted for different routing objectives. The framework is tested on a set of commonly used benchmark circuits and compared with a previous multilevel routing framework. Experimental results show that the presented framework obtains significantly better routing solutions than the previous one considering circuit performance, routing completion rate, and runtime.

Experimental study on the working performance of different milling tools for multistage fracturing ball seats

To achieve the secondary production in multistage fracturing wells of tight oil,milling tools are usually used to remove the multistage fracturing ball seats to achieve production with a large diameter in later.In this paper,first of all,the working mechanism of milling tools for multistage fracturing ball seats was studied and a mechanical analysis model of single abrasive grain was established.Then,an experimental system for milling tools was developed,and the experimental tests of the flat,the blade,and the slope milling tool were conducted in order.Besides,the morphology of chips and the surface morphology of the workpiece after the experiment were analyzed.Also,the working performance of milling tools was evaluated from the perspectives of working safety,working efficiency,and wear resistance of the milling tool.The results show that the torque of the milling tool increases nonlinearly with the increase in the cutting depth of the abrasive grain and increases linearly with the increase in the cutting width.Also,the chips are irregular particles and the size is mainly from 10 to 50μm.So,the chips should be pumped up with a small pump pressure and a large displacement.Besides this,the cutting depths of the abrasive grains are from 216.20 to 635.47μm and the bottom surface of the milling tool should be eccentric to avoid the zero point of cutting speed.Furthermore,the torque of the slope milling tool is 23.8%larger than that of the flat milling tool,which is also 30.4%smaller than that of the blade milling tool.Compared with the flat milling tool,the working efficiency of the blade milling tool improves by 79.9%and the slope milling tool improves by 111.1%.Also,the wear resistance of the blade milling tool decreases by 102.7%,while the slope milling tool declines by 32.6%when compared with the flat milling tool.Therefore,the slope milling tool has the characteristics of moderate torque,stable working conditions,the highest working efficiency,and fine wear resistance,which is preferably used to mill multistage fracturing ball seats.This study provides a theoretical basis and guidance for milling multistage fracturing ball seats on-site and realizing production with a large diameter in later stages of multistage fracturing wells.

An integrative review of the influence of job strain and coping on nurses'work performance:Understanding the gaps in oncology nursing research

Nursing is known to be a stressful profession that can lead to physical and psychological health issues and behavioural problems.In oncology,workload among nurses is believed to be increasing in conjunction with rapidly increasing numbers of patients with cancer and staff shortages worldwide,therefore it is essential to sustain a quality oncology nurse workforce.Numerous studies have presented evidence on job strain,effects of coping strategies,and nurses'work performance within healthcare settings,but few have focused on oncology settings and none of these on nurses working in Saudi Arabia.The purpose of this review was to summarize empirical and theoretical evidence concerning job-related stressors in nurses,particularly oncology nurses,and the interrelationships among job strain,coping strategies,and work performance in this population.Search strategies identified studies published on studies in peer-reviewed journals from 2004 to 2016.Twenty-five nursing studies were found examining the relationships among the concepts of interest.Common job-related stressors among oncology nurses were high job demands,dealing with death/dying,lack of job control,and interpersonal conflicts at work.Job strain was found to be significantly linked to coping strategies,and negatively associated with work performance among nurses in general.There is no existing empirical evidence to support the relationship between coping strategies and work performance among oncology nurses.The present evidence is limited,and a considerable amount of research is required in the future to expand the oncology nursing literature.Research is needed to investigate job-related stressors and their effects on oncology nurses.

Association of post-traumatic stress disorder and work performance: A survey from an emergency medical service, Karachi, Pakistan

BACKGROUND: The purpose of the study was to explore the association between posttraumatic stress disorder(PTSD) and work performance of emergency medical services personnel in Karachi, Pakistan.METHODS: Emergency medical service personnel were screened for potential PTSD using Impact of Event Scale-Revised(IES-R). Work performance was assessed on the basis of fi ve variables: number of late arrivals to work, number of days absent, number of days sick, adherence to protocol, and patient satisfaction over a period of 3 months. In order to model outcomes like the number of late arrivals to work, days absent and days late, negative binomial regression was applied, whereas logistic regression was applied for adherence to protocol and linear for patient satisfaction scores.RESULTS: Mean scores of PTSD were 24.0±12.2. No association was found between PTSD and work performance measures: number of late arrivals to work(RRadj 0.99; 0.98–1.00), days absent(RRadj 0.98; 0.96–0.99), days sick(RRadj 0.99; 0.98–1.00), adherence to protocol(ORadj 1.01; 0.99–1.04) and patient satisfaction(β 0.001%–0.03%) after adjusting for years of formal schooling, living status, coping mechanism, social support, working hours, years of experience and anxiety or depression.CONCLUSION: No statistically significant association was found between PTSD and work performance amongst EMS personnel in Karachi, Pakistan.

IMPROVING ELEVATOR MAINTENANCE PERFORMANCE: WORK STUDY APPROACH

Strategic quality management is implemented in a local elevator company. Quality strategies, policies and goals focusing on customers′ needs are clearly established. Continuous quality improvement has been adopted as the corporate strategy to improve its performance and competitive edge. This paper presents the successful implementation of a quality improvement project using the work study approach. On the completion of the project, the rate of serious defects which may lead to an accident is reduced by 55%, i.e., from 2%￣0.9% over a period of two years. A total of HK$ 1.1 million spent on the project is justified when compared with the reduction of financial risks involved. The success of the project demonstrates that the work study approach could be an effective tool for improving the performance of maintenance work.

Effect of working parameters on performance characteristics of hydrostatic turntable by using FSI-thermal model

Effects of working parameters on performance characteristics of hydrostatic turntable are researched by applying the fluid-structure-thermal coupled model.Fluid-structure interaction(FSI)technique and computational fluid dynamics(CFD)method are both employed by this new model,and thermal effects are also considered.Hydrostatic turntable systems with a series of oil supply pressures,various oil recess depth and several surface roughness parameters are studied.Performance parameters,such as turntable displacement,system flow rate,temperature rise of lubrication,stiffness and damping coefficients,are derived from different working parameters(rotational speed of turntable and exerted external load)of the hydrostatic turntable.Numerical results obtained from this FSI-thermal model are presented and discussed,and theoretical predictions are in good agreement with the experimental data.Therefore,this developed model is a very useful tool for studying hydrostatic turntables.The calculation results show that in order to obtain better performance,a rational selection of the design parameters is essential.

Performance Evaluation for SDN Deployment: an Approach Based on Stochastic Network Calculus

The Open Flow implementations(SDNs) have been deployed increasingly on varieties of networks in research institutions as well as commercial institutions. To develop an Open Flow implementation, it is required to understand the performance of the network. A few benchmark tools(e.g., Cbench and OFlops) can be used to measure the network performance, while these tools take considerable time to simulate traffic behaviors and generate the required results,therefore extending the development time. In this paper, we present an analytical model, which is based on stochastic network calculus theory, for evaluating the performance of switch to controller.The previous studies show that stochastic network calculus can provide realistic emulation of real network traffic behaviors. Our model is evaluated by using both simulation tool and realistic testbed.The results show the stochastic network calculus based analysis model can realistically measure the network performance of the end-to-end properties between controller and switch.