Dynamic changes of cellular immune function and individualized adjustments of immunosup-pressant for the management of severe infection after liver transplantation

Objective To explore the dynamic changes of the cellular immune function in severe infection after liver transplantation,and to guide the individualized immunology adjustment. Methods 378 cases of liver transplantation were analyzed retrospectively. Seventy - four cases ( infection group) suffered serious infection,including 54 cases cured ( cure group) ,20 cases died (

Dynamic capacity balance between stripping and mining of a dragline stripping system

The reliability factors of a dragline stripping system were analyzed using system reliability theory to improve its reliability and ensure the stability of surface coal mine production.The relationship between the dragline scheduled stripping volume with both the weighted average thickness of the coal seam and the height of the blast casting bench was derived.The results show that the reliability of the dragline stripping system decreases with the weighted average thickness of the coal seam and increases with a reduced casting blast bench height and monthly raw coal production capacity.Furthermore,the dynamic monthly advance distance of the dragline stripping system was found,a new concept of the dragline stripping system reliability and measurement methodology were proposed,and a prediction model for the dragline production capacity was established using a generalized regression neural network.In addition,the steps and processes of the reliability were improved.The study on the Heidaigou surface mine shows that the height of the casting blast bench should be reduced to 10.5 m when its raw coal production capacity is improved from 20 to 30 Mt/a.During the normal production of surface coal,the system reliability can be improved by dynamically determining the weighted average coal seam thickness,accurately predicting the dragline production capacity,and taking other corresponding measures.

Bioinspired 4D Printing Shape-Memory Polyurethane Rhinoplasty Prosthesis for Dynamic Aesthetic Adjustment

The disparity between the postoperative outcomes of rhinoplasty and the expected results frequently necessitates secondary or multiple surgeries as a compensatory measure,greatly diminishing patient satisfaction.However,there is renewed optimism for addressing these challenges through the innovative realm of Four-Dimensional(4D)printing.This groundbreaking technology enables three-dimensional objects with shape-memory properties to undergo predictable transformations under specific external stimuli.Consequently,implants crafted using 4D printing offer significant potential for dynamic adjustments.Inspired by worms in our research,we harnessed 4D printing to fabricate a Shape-Memory Polyurethane(SMPU)for use as a nasal augmentation prosthesis.The choice of SMPU was guided by its Glass Transition Temperature(Tg),which falls within the acceptable temperature range for the human body.This attribute allowed for temperature-responsive intraoperative self-deformation and postoperative remodeling.Our chosen animal model for experimentation was rabbits.Taking into account the anatomical structure of the rabbit nose,we designed and produced nasal augmentation prostheses with superior biocompatibility.These prostheses were then surgically implanted in a minimally invasive manner into the rabbit noses.Remarkably,they exhibited successful temperature-controlled in-surgery self-deformation according to the predetermined shape and non-invasive remodeling within a mere 9 days post-surgery.Subsequent histological evaluations confirmed the practical viability of these prostheses in a living organism.Our research findings posit that worm-inspired 4D-printed SMPU nasal prostheses hold significant promise for achieving dynamic aesthetic adjustments.

Temperature-Based Fan Speed Optimization Strategy

As energy efficiency and indoor comfort increasingly become key standards in modern residential and office environments,research on intelligent fan speed control systems has become particularly important.This study aims to develop a temperature-feedback-based fan speed optimization strategy to achieve higher energy efficiency and user comfort.Firstly,by analyzing existing fan speed control technologies,their main limitations are identified,such as the inability to achieve smooth speed transitions.To address this issue,a BP-PID speed control algorithm is designed,which dynamically adjusts fan speed based on indoor temperature changes.Experimental validation demonstrates that the designed system can achieve smooth speed transitions compared to traditional fan systems while maintaining stable indoor temperatures.Furthermore,the real-time responsiveness of the system is crucial for enhancing user comfort.Our research not only demonstrates the feasibility of temperature-based fan speed optimization strategies in both theory and practice but also provides valuable insights for energy management in future smart home environments.Ultimately,this research outcome will facilitate the development of smart home systems and have a positive impact on environmental sustainability.

An Empirical Analysis on the Stable Return of Resident Funds

This paper mainly studies how investors invest in funds to obtain high returns while avoiding risks.Firstly,from the perspective of portfolio investment,this paper introduces the traditional Markowitz mean-variance model and capital asset pricing model(CAPM),then selects four funds from different industries by MATLAB program in Sina Finance and Economics Network for application analysis from which the optimal portfolio point can be obtained under the combination of efficient frontier and capital allocation line.Subsequently,by analyzing the returns of long-term holdings and short-term operations of Noan Growth Hybrid Fund,it is confirmed that long-term holding funds can better cope with the changing market so as to obtain more stable returns.Finally,this paper discusses the dynamic adjustments of asset portfolio.Resident investors are supposed to take into account the market situation and the changes of the fund itself to adjust the holding fund portfolio.Based on the research in this paper,resident investors ought to combine investment funds to diversify risk allocation and make long-term holding plans according to their risk tolerance.At the same time,they should also make appropriate dynamic adjustments when the external environment changes to ensure long-term benefits.

Approach to inter-satellite time synchronization for micro-satellite cluster

Micro-satellite cluster enables a whole new class of missions for communications, remote sensing, and scientific research for both civilian and military purposes. Synchronizing the time of the satellites in a cluster is important for both cluster sensing capabilities and its autonomous operating. However, the existing time synchronization methods are not suitable for microsatellite cluster, because it requires too many human interventions and occupies too much ground control resource. Although, data post-process may realize the equivalent time synchronization, it requires processing time and powerful computing ability on the ground, which cannot be implemented by cluster itself. In order to autonomously establish and maintain the time benchmark in a cluster, we propose a compact time difference compensation system（TDCS）, which is a kind of time control loop that dynamically adjusts the satellite reference frequency according to the time difference. Consequently, the time synchronization in the cluster can be autonomously achieved on-orbit by synchronizing the clock of other satellites to a chosen one＇s. The experimental result shows that the standard deviation of time synchronization is about 102 ps when the carrier to noise ratio（CNR） is 95 d BHz, and the standard deviation of corresponding frequency difference is approximately0.36 Hz.

How and Why Do Firms Adjust Their Cash Holdings toward Targets？ Evidence from China

We examine the dynamic adjustment of cash holdings of publicly traded Chinese firms during 1998-2006. The empirical evidence is supportive of the dynamic trade-off theory of cash holdings. In particular, there is strong evidence of asymmetric adjustments, i.e., adjustments from above the target are significantly faster than those from below. Moreover, the speeds of adjustment （SOA） are heterogeneous for firms facing differential adjustment costs. More specifically, the adjustment speed is higher in firms with bank lines of credit, positively related to the deviation from the target, but it is negatively related to firm size. Furthermore, in terms of adjustment method, firms make adjustments to their targets primarily through debt and equity financing when they are in cash shortage, On the other hand, the dividend payments play a minimal role in it. Lastly, in terms of motives for adjustment, we find that the precautionary motive arising from financial constraints well explains the cash holdings adjustment behaviors of Chinese listed firms.

Dynamic Transition of Exchange Rate Regime in China

Thepaper considers the optimal transition path for China＇s exchange rate regime. How can China successfully make the shift from the current dollar peg regime to a more desirable regime, whether a basket peg or a floating regime？ To answer this question, we develop a dynamic small open economy general equilibrium model. We construct four transition policies based on a basket peg or a floating regime and compare the welfare gains of these policies relative to maintaining the dollar peg regime. Two main results are derived from the quantitative analysis using Chinese data from 1999Q1 to 2010Q4. First, following a gradual adjustment to a basket peg regime is the most appropriate path for China to take, with minimal welfare losses associated with the shift in the exchange rate regime. Second, a sudden shift to the basket peg is the second best solution, and is superior to a sudden shift to floating because the monetary authority can efficiently determine optimal weights to attach to currencies in the basket to achieve policy goals once they adopt a basket peg regime.

Plotinus:A Satellite Internet Digital Twin System

The development of space-air-ground integrated networks (SAGIN) requires sophisticated satellite Internet emulation tools that can handle complex, dynamic topologies and offer in-depth analysis. Existing emulation platforms struggle with challenges like the need for detailed implementation across all network layers, real-time response, and scalability. This paper proposes a digital twin system based on microservices for satellite Internet emulation,namely Plotinus,which aims to solve these problems. Plotinus features a modular design, allowing for easy replacement of the physical layer to emulate different aerial vehicles and analyze channel interference. It also enables replacing of path computation methods to simplify testing and deploying algorithms. In particular, Plotinus allows for real-time emulation with live network traffic,enhancing practical network models. The evaluation result shows Plotinus’s effective emulation of dynamic satellite networks with real-world devices. Its adaptability for various communication models and algorithm testing highlights Plotinus’s role as a vital tool for developing and analyzing SAGIN systems, offering a cross-layer,real-time,and scalable digital twin system.

A new dynamic bacterial foraging optimization and its application on model reduction

Inspired by the foraging behavior of E.coli bacteria,bacterial foraging optimization(BFO)has emerged as a powerful technique for solving optimization problems.However,BFO shows poor performance on complex and high-dimensional optimization problems.In order to improve the performance of BFO,a new dynamic bacterial foraging optimization based on clonal selection(DBFO-CS)is proposed.Instead of fixed step size in the chemotaxis operator,a new piecewise strategy adjusts the step size dynamically by regulatory factor in order to balance between exploration and exploitation during optimization process,which can improve convergence speed.Furthermore,reproduction operator based on clonal selection can add excellent genes to bacterial populations in order to improve bacterial natural selection and help good individuals to be protected,which can enhance convergence precision.Then,a set of benchmark functions have been used to test the proposed algorithm.The results show that DBFO-CS offers significant improvements than BFO on convergence,accuracy and robustness.A complex optimization problem of model reduction on stable and unstable linear systems based on DBFO-CS is presented.Results show that the proposed algorithm can efficiently approximate the systems.

Dynamics of financial leverage across firm life cycle in Chinese firms:An empirical investigation using dynamic panel data model

This study tries to investigate how firms adjust their leverage policy across the firm’s life cycle.For this purpose the study uses an extensive set of data of 867 A listed Chinese non-financial firms over a 19-year years period(1996-2014).The study employs Arellano-Bover/Blundell-Bond dynamic panel data model to estimate adjustment rate of leverage and its determinants in three different life stages of Chinese firms.We find that adjustment rate of leverage varies for different life stages.In accordance with trade off theory of capital structure this study reports a low-high-low pattern of leverage across growth,maturity and decline stage of firms’life respectively.For total leverage,dynamic panel data reports highest adjustment rate for growing firms,followed by mature firms and firms in declining stage of their life.Both short term and long term leverage report similar pattern of leverage’s adjustment rate across the three stages of life cycle.The study provides useful insight in a unique market setting of Chinese financial markets.

Fully Distributed Risk-based Robust Reserve Scheduling for Bulk Hybrid AC-DC Systems

To enhance the cost-effectiveness of bulk hybrid AC-DC power systems and promote wind consumption,this paper proposes a two-stage risk-based robust reserve scheduling(RRRS)model.Different from traditional robust optimization,the proposed model applies an adjustable uncertainty set rather than a fixed one.Thereby,the operational risk is optimized together with the dispatch schedules,with a reasonable admissible region of wind power obtained correspondingly.In addition,both the operational base point and adjustment capacity of tielines are optimized in the RRRS model,which enables reserve sharing among the connected areas to handle the significant wind uncertainties.Based on the alternating direction method of multipliers(ADMM),a fully distributed framework is presented to solve the RRRS model in a distributed way.A dynamic penalty factor adjustment strategy(DPA)is also developed and applied to enhance its convergence properties.Since only limited information needs to be exchanged during the solution process,the communication burden is reduced and regional information is protected.Case studies on the 2-area 12-bus system and 3-area 354-bus system illustrate the effectiveness of the proposed model and approach.

Decomposition-Based Multi-Objective Optimization for Energy-Aware Distributed Hybrid Flow Shop Scheduling with Multiprocessor Tasks

This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks(EADHFSPMT)by considering two objectives simultaneously,i.e.,makespan and total energy consumption.It consists of three sub-problems,i.e.,job assignment between factories,job sequence in each factory,and machine allocation for each job.We present a mixed inter linear programming model and propose a Novel MultiObjective Evolutionary Algorithm based on Decomposition(NMOEA/D).We specially design a decoding scheme according to the characteristics of the EADHFSPMT.To initialize a population with certain diversity,four different rules are utilized.Moreover,a cooperative search is designed to produce new solutions based on different types of relationship between any solution and its neighbors.To enhance the quality of solutions,two local intensification operators are implemented according to the problem characteristics.In addition,a dynamic adjustment strategy for weight vectors is designed to balance the diversity and convergence,which can adaptively modify weight vectors according to the distribution of the non-dominated front.Extensive computational experiments are carried out by using a number of benchmark instances,which demonstrate the effectiveness of the above special designs.The statistical comparisons to the existing algorithms also verify the superior performances of the NMOEA/D.

Adaptive watermark generation mechanism based on time series prediction for stream processing

The data stream processing framework processes the stream data based on event-time to ensure that the request can be responded to in real-time.In reality,streaming data usually arrives out-of-order due to factors such as network delay.The data stream processing framework commonly adopts the watermark mechanism to address the data disorderedness.Watermark is a special kind of data inserted into the data stream with a timestamp,which helps the framework to decide whether the data received is late and thus be discarded.Traditional watermark generation strategies are periodic;they cannot dynamically adjust the watermark distribution to balance the responsiveness and accuracy.This paper proposes an adaptive watermark generation mechanism based on the time series prediction model to address the above limitation.This mechanism dynamically adjusts the frequency and timing of watermark distribution using the disordered data ratio and other lateness properties of the data stream to improve the system responsiveness while ensuring acceptable result accuracy.We implement the proposed mechanism on top of Flink and evaluate it with realworld datasets.The experiment results show that our mechanism is superior to the existing watermark distribution strategies in terms of both system responsiveness and result accuracy.

Hybrid gray wolf optimization-cuckoo search algorithm for RFID network planning

In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network planning(RNP)has become the primary concern.Compared with the traditional methods,meta-heuristic method is widely used in RNP.Aiming at the target requirements of RFID,such as fewer readers,covering more tags,reducing the interference between readers and saving costs,this paper proposes a hybrid gray wolf optimization-cuckoo search(GWO-CS)algorithm.This method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation area.Compared with particle swarm optimization(PSO)algorithm,cuckoo search(CS)algorithm and gray wolf optimization(GWO)algorithm under the same experimental conditions,the coverage of GWO-CS is 9.306%higher than that of PSO algorithm,6.963%higher than that of CS algorithm,and 3.488%higher than that of GWO algorithm.The results show that the GWO-CS algorithm cannot only improve the global search range,but also improve the local search depth.