Clinical course and outcome of adult patients with primary focal segmental glomerulosclerosis with kidney function loss on presentation

BACKGROUND Kidney function loss or renal insufficiency indicated by elevated creatinine levels and/or an estimated glomerular filtration rate(eGFR)<60 mL/minute/1.73 m²at presentation in patients with primary focal segmental glomerulosclerosis(FSGS)is commonly seen as a poor prognostic marker for kidney survival.However,a pre>vious study from our center suggested this may be due to hemodynamic factors.AIM To observe the clinical and biochemical parameters,treatment response,kidney survival,and overall outcomes of adult patients with primary FSGS presenting with kidney function insufficiency.METHODS This retrospective observational study was conducted at the Department of Nephrology,Sindh Institute of Urology and Transplantation,Karachi,Pakistan,from January 1995 to December 2017.During this period,401 biopsy-proven primary FSGS patients were identified,of which 98(24.4%)presented with kidney function loss or renal insufficiency defined as eGFR<60 mL/minute/1.73 m²at presentation and were studied in detail.RESULTS Among the 98 patients with renal function loss on presentation,the mean age was 30.9 years±13.6 years with a male-to-female ratio of 2.5:1.The mean serum creatinine level was 2.2 mg/dL±1.3 mg/dL and mean eGFR 37.1 mL/minute/1.73 m2±12.8 mL/minute/1.73 m2.The mean 24-hour urinary protein excretion was 5.9 g/day±4.0 g/day,and the mean serum albumin was 2.1 g/dL±1.0 g/dL(median:1.5 g/dL).The mean systolic blood pressure(BP)was 132.7 mmHg±19.8 mmHg,and the mean diastolic BP was 87.4 mmHg±12.7 mmHg.Steroid treatment was given to 81(82.6%)of 98 patients for an average duration of 19.9 weeks±14.4 weeks,with a mean total steroid dose of 4.4 g±1.5 g.Treatment response showed that 20(24.6%)patients achieved complete remission,9(11.1%)achieved partial remission,and 52(64.1%)did not respond.The baseline eGFR was significantly lower in the non-responsive group(P=0.006).The distribution of FSGS variants was also significantly different among steroid-responsive and non-responsive groups(P=0.012).CONCLUSION Renal function loss in FSGS patients at presentation does not necessarily indicate irreversible kidney function loss and a significant number of patients respond to appropriate treatment of the underlying disease.

Multi-Objective Optimization of High Torque Density Segmented PM Consequent Pole Flux Switching Machine with Flux Bridge

Due to double salient structure,Flux Switching Machines(FSMs)are preferred for brushless AC high speed applications.Permanent Magnet(PM)FSMs(PM-FSMs)are suited applicants where high torque density(Tden)and power density(Pden)are the utmost requisite.However conventional PM-FSMs utilizes excessive rare earth PM volume VPM,higher cogging torque Tcog,high torque ripples(Trip)and comparatively lower(Tden)and Pden due to flux leakage.To overcome the aforesaid demerits,a new high(Tden)Segmented PM Consequent Pole(CP)FSM(SPMCPFSM)with flux bridge and barrier is proposed which successfully reduces VPM by 46.52%and PM cost by 46.48%.Moreover,Multi-Objective Optimization(MOO)examines electromagnetic performance due to variation in geometric parameters for global optimum parameters with key metric such as flux linkage(Φpp),flux harmonics(ΦTHD)average torque(Tavg),Tcog,Trip,Tden,average power(Pavg)and Pden.Analysis reveals that MOO improveΦpp by 22.68%,boost Tavg by 11.41%,enhanced Pavg by 4.55%and increased Tden and Pden by 11.41%.Detailed electromagnetic performance comparison with existing state of the art shows that proposed SPMCPFSM offer Tavg maximum up to 88.8%,truncate Trip up to 24.8%,suppress Tcog up to 22.74%,and results 2.45 times Tden and Pden.

Multi-objective forest harvesting under sustainable and economic principles

Selective logging is well-recognized as an effective practice in sustainable forest management.However,the ecological efficiency or resilience of the residual stand is often in doubt.Recovery time depends on operational variables,diversity,and forest structure.Selective logging is excellent but is open to changes.This may be resolved by mathematical programming and this study integrates the economic-ecological aspects in multi-objective function by applying two evolutionary algorithms.The function maximizes remaining stand diversity,merchantable logs,and the inverse of distance between trees for harvesting and log landings points.The Brazilian rainforest database(566 trees)was used to simulate our 216-ha model.The log landing design has a maximum volume limit of 500 m3.The nondominated sorting genetic algorithm was applied to solve the main optimization problem.In parallel,a sub-problem(p-facility allocation)was solved for landing allocation by a genetic algorithm.Pareto frontier analysis was applied to distinguish the gradientsα-economic,β-ecological,andγ-equilibrium.As expected,the solutions have high diameter changes in the residual stand(average removal of approximately 16 m^(3) ha^(-1)).All solutions showed a grouping of trees selected for harvesting,although there was no formation of large clearings(percentage of canopy removal<7%,with an average of 2.5 ind ha^(-1)).There were no differences in floristic composition by preferentially selecting species with greater frequency in the initial stand for harvesting.This implies a lower impact on the demographic rates of the remaining stand.The methodology should support projects of reduced impact logging by using spatial-diversity information to guide better practices in tropical forests.

Multi-objective optimization of rolling schedule based on cost function for tandem cold mill

In terms of tandem cold mill productivity and product quality, a multi-objective optimization model of rolling schedule based on cost fimction was proposed to determine the stand reductions, inter-stand tensions and rolling speeds for a specified product. The proposed schedule optimization model consists of several single cost fi.mctions, which take rolling force, motor power, inter-stand tension and stand reduction into consideration. The cost function, which can evaluate how far the rolling parameters are from the ideal values, was minimized using the Nelder-Mead simplex method. The proposed rolling schedule optimization method has been applied successfully to the 5-stand tandem cold mill in Tangsteel, and the results from a case study show that the proposed method is superior to those based on empirical formulae.

Multi-Objective Redundancy Optimization of Continuous-Point Robot Milling Path in Shipbuilding

The 6-DOF manipulator provides a new option for traditional shipbuilding for its advantages of vast working space,low power consumption,and excellent flexibility.However,the rotation of the end effector along the tool axis is functionally redundant when using a robotic arm for five-axis machining.In the process of ship construction,the performance of the parts’protective coating needs to bemachined tomeet the Performance Standard of Protective Coatings(PSPC).The arbitrary redundancy configuration in path planning will result in drastic fluctuations in the robot joint angle,greatly reducing machining quality and efficiency.There have been some studies on singleobjective optimization of redundant variables,However,the quality and efficiency of milling are not affected by a single factor,it is usually influenced by several factors,such as the manipulator stiffness,the joint motion smoothness,and the energy consumption.To solve this problem,this paper proposed a new path optimization method for the industrial robot when it is used for five-axis machining.The path smoothness performance index and the energy consumption index are established based on the joint acceleration and the joint velocity,respectively.The path planning issue is formulated as a constrained multi-objective optimization problem by taking into account the constraints of joint limits and singularity avoidance.Then,the path is split into multiple segments for optimization to avoid the slow convergence rate caused by the high dimension.An algorithm combining the non-dominated sorting genetic algorithm(NSGA-II)and the differential evolution(DE)algorithm is employed to solve the above optimization problem.The simulations validate the effectiveness of the algorithm,showing the improvement of smoothness and the reduction of energy consumption.

Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

Nuclearmagnetic resonance imaging of breasts often presents complex backgrounds.Breast tumors exhibit varying sizes,uneven intensity,and indistinct boundaries.These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation.Thus,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms.Initially,the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs.Subsequently,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor segmentation.We incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion techniques.Additionally,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel domains.Furthermore,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional loss.Themethod was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the benchmarkU-Net.Experimental results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.

Multi-objective Function Optimization for Environmental Control of a Greenhouse Based on a RBF and NSGA-Ⅱ

To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming

In this paper, we present an algorithm to solve the inequality constrained multi-objective programming (MP) by using a penalty function with objective parameters and constraint penalty parameter. First, the penalty function with objective parameters and constraint penalty parameter for MP and the corresponding unconstraint penalty optimization problem (UPOP) is defined. Under some conditions, a Pareto efficient solution (or a weakly-efficient solution) to UPOP is proved to be a Pareto efficient solution (or a weakly-efficient solution) to MP. The penalty function is proved to be exact under a stable condition. Then, we design an algorithm to solve MP and prove its convergence. Finally, numerical examples show that the algorithm may help decision makers to find a satisfactory solution to MP.

Optimality for Multi-Objective Programming Involving Arcwise Connected d-Type-I Functions

This paper deals with the optimality conditions and dual theory of multi-objective programming problems involving generalized convexity. New classes of generalized type-I functions are introduced for arcwise connected functions, and examples are given to show the existence of these functions. By utilizing the new concepts, several sufficient optimality conditions and Mond-Weir type duality results are proposed for non-differentiable multi-objective programming problem.

LPI radar signal detection based on the combination of FFT and segmented autocorrelation plus PAHT

This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of linear frequency modulation, phase code, and frequency code. Firstly, it improves the coherent integration of LPI radar signals by adding the periodicity of the ambiguity function. Then, it develops a frequency domain detection method based on fast Fourier transform (FFT) and segmented autocorrelation function to detect signals without features of linear frequency modulation by virtue of the distribution characteristics of noise signals in the frequency domain. Finally, this paper gives a verification of the performance of the method for different signal-to-noise ratios by conducting simulation experiments, and compares the method with existing ones. Additionally, this method is characterized by the straightforward calculation and high real-time performance, which is conducive to better detecting all kinds of LPI radar signals.

Three-dimensional visualization of the functional fascicular groups of a long-segment peripheral nerve

The three-dimensional（3D） visualization of the functional bundles in the peripheral nerve provides direct and detailed intraneural spatial information. It is useful for selecting suitable surgical methods to repair nerve defects and in optimizing the construction of tissue-engineered nerve grafts. However, there remain major technical hurdles in obtaining, registering and interpreting 2D images, as well as in establishing 3D models. Moreover, the 3D models are plagued by poor accuracy and lack of detail and cannot completely reflect the stereoscopic microstructure inside the nerve. To explore and help resolve these key technical problems of 3D reconstruction, in the present study, we designed a novel method based on re-imaging techniques and computer image layer processing technology. A 20-cm ulnar nerve segment from the upper arm of a fresh adult cadaver was used for acetylcholinesterase（ACh E） staining. Then, 2D panoramic images were obtained before and after ACh E staining under the stereomicroscope. Using layer processing techniques in Photoshop, a space transformation method was used to fulfill automatic registration. The contours were outlined, and the 3D rendering of functional fascicular groups in the long-segment ulnar nerve was performed with Amira 4.1 software. The re-imaging technique based on layer processing in Photoshop produced an image that was detailed and accurate. The merging of images was accurate, and the whole procedure was simple and fast. The least square support vector machine was accurate, with an error rate of only 8.25%. The 3D reconstruction directly revealed changes in the fusion of different nerve functional fascicular groups. In conclusion. The technique is fast with satisfactory visual reconstruction.

Image segmentation based on Mumford-Shah functional

In this paper, the authors propose a new model for active contours segmentation in a given image, based on Mumford-Shah functional (Mumford and Shah, 1989). The model is composed of a system of differential and integral equations. By the experimental results we can keep the advantages of Chan and Vese's model (Chan and Vese, 2001 ) and avoid the regularization for Dirac function. More importantly, in theory we prove that the system has a unique viscosity solution.

A vague-set-based fuzzy multi-objective decision making model for bidding purchase

A vague-set-based fuzzy multi-objective decision making model is developed for evaluating bidding plans in a bid- ding purchase process. A group of decision-makers (DMs) first independently assess bidding plans according to their experience and preferences, and these assessments may be expressed as linguistic terms, which are then converted to fuzzy numbers. The resulting decision matrices are then transformed to objective membership grade matrices. The lower bound of satisfaction and upper bound of dissatisfaction are used to determine each bidding plan’s supporting, opposing, and neutral objective sets, which together determine the vague value of a bidding plan. Finally, a score function is employed to rank all bidding plans. A new score function based on vague sets is introduced in the model and a novel method is presented for calculating the lower bound of sat- isfaction and upper bound of dissatisfaction. In a vague-set-based fuzzy multi-objective decision making model, different valua- tions for upper and lower bounds of satisfaction usually lead to distinct ranking results. Therefore, it is crucial to effectively contain DMs’ arbitrariness and subjectivity when these values are determined.

Application of radial basis functions to evolution equations arising in image segmentation

In this paper, radial basis functions are used to obtain the solution of evolution equations which appear in variational level set method based image segmentation. In this method, radial basis functions are used to interpolate the implicit level set function of the evolution equation with a high level of accuracy and smoothness. Then, the original initial value problem is discretized into an interpolation problem. Accordingly, the evolution equation is converted into a set of coupled ordinary differential equations, and a smooth evolution can be retained. Compared with finite difference scheme based level set approaches, the complex and costly re-initialization procedure is unnecessary. Numerical examples are also given to show the efficiency of the method.

Overview of multi-objective optimization methods

To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.

Performance driven expression mapping based on segmented examples

We present a method that combines performance-driven method with segmented 3D blendshape models to animate a face. First we prepare key sample examples and corresponding key target examples. Next we segment the whole face into two regions, for each region we reduce dimensionality of source examples using PAC into abstract space which is defined by truncated PCA eigen- vectors. Then for each example we fix the cardinal base function, which can determine the weight of the target example. Finally, in the animation stage we compute the weight of each example for each frame and add the weighted displacement vectors of each re- gion on the general face model.

Finite Element Analysis of Shrinkage in the Interface of Functionally Graded Concrete Segment Used in Shield Tunneling

In functionally graded materials (FGM), the problem of interface stability caused by the volume deformation is commonly regarded as the key factor for its performance. Based on test results, in terms of finite element method (FEM) this paper analyzed problems in the shrinkage of functionally graded material interface of shield concrete segment, which was designed and produced by the principle of functionally graded materials. In the analysis model, the total shrinkage of concrete was converted into the thermal shrinkage by means of the method of 'Equivalent Temperature Difference'. Consequently, the shrinkage stress of interface layer was calculated and compared with the bond strength of interface layer. The results indicated that the volume deformation of two-phase materials of functionally graded concrete (FGC) segment, which were the concrete cover and the concrete structure layer, showed better compatibility and the tension stress of interface layer, which was resulted from the shrinkage of concrete and calculated by ANSYS, was less than the bond strength of interface layer. Therefore, the interface stability of functionally graded concrete segment was good and the sliding deformation of interface layer would not generate.

A Study on the Multi-Objective Optimization Method of Brackets in Ship Structures

The shape and size optimization of brackets in hull structures was conducted to achieve the simultaneous reduction of mass and high stress,where the parametric finite element model was built based on Patran Command Language codes.The optimization procedure was executed on Isight platform,on which the linear dimensionless method was introduced to establish the weighted multi-objective function.The extreme processing method was applied and proved effective to normalize the objectives.The bracket was optimized under the typical single loads and design waves,accompanied by the different proportions of weights in the objective function,in which the safety factor function was further established,including yielding,buckling,and fatigue strength,and the weight minimization and safety maximization of the bracket were obtained.The findings of this study illustrate that the dimensionless objectives share equal contributions to the multi-objective function,which enhances the role of weights in the optimization.

Nonlinear analysis on dynamic buckling of eccentrically stiffened functionally graded material toroidal shell segment surrounded by elastic foundations in thermal environment and under time-dependent torsional loads

The nonlinear analysis with an analytical approach on dynamic torsional buckling of stiffened functionally graded thin toroidal shell segments is investigated. The shell is reinforced by inside stiffeners and surrounded by elastic foundations in a thermal environment and under a time-dependent torsional load. The governing equations are derived based on the Donnell shell theory with the yon Karman geometrical nonlinearity, the Stein and McElman assumption, the smeared stiffeners technique, and the Galerkin method. A deflection function with three terms is chosen. The thermal parameters of the uniform temperature rise and nonlinear temperature conduction law are found in an explicit form. A closed-form expression for determining the static critical torsional load is obtained. A critical dynamic torsional load is found by the fourth-order Runge-Kutta method and the Budiansky-Roth criterion. The effects of stiffeners, foundations, material, and dimensional parameters on dynamic responses of shells are considered.

Segmented second algorithm of empirical mode decomposition

A new algorithm, named segmented second empirical mode decomposition （EMD） algorithm, is proposed in this paper in order to reduce the computing time of EMD and make EMD algorithm available to online time-frequency analysis. The original data is divided into some segments with the same length. Each segment data is processed based on the principle of the first-level EMD decomposition. The algorithm is compared with the traditional EMD and results show that it is more useful and effective for analyzing nonlinear and non-stationary signals.