Selection of dialysis methods for end-stage kidney disease patients with diabetes

The increasing prevalence of diabetes has led to a growing population of endstage kidney disease(ESKD)patients with diabetes.Currently,kidney transplantation is the best treatment option for ESKD patients;however,it is limited by the lack of donors.Therefore,dialysis has become the standard treatment for ESKD patients.However,the optimal dialysis method for diabetic ESKD patients remains controversial.ESKD patients with diabetes often present with complex conditions and numerous complications.Furthermore,these patients face a high risk of infection and technical failure,are more susceptible to malnutrition,have difficulty establishing vascular access,and experience more frequent blood sugar fluctuations than the general population.Therefore,this article reviews nine critical aspects:Survival rate,glucose metabolism disorder,infectious complications,cardiovascular events,residual renal function,quality of life,economic benefits,malnutrition,and volume load.This study aims to assist clinicians in selecting individualized treatment methods by comparing the advantages and disadvantages of hemodialysis and peritoneal dialysis,thereby improving patients’quality of life and survival rates.

Improved Double Deep Q Network Algorithm Based on Average Q-Value Estimation and Reward Redistribution for Robot Path Planning

By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots.However,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data.Targeting those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed.First,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q network.Next,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory information.Additionally,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality data.Finally,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random environments.The experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average steps.The performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random environments.It not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.

Comparative Analysis of Improving Effects of 12 Maize Populations with Improved S_1 Selection Method

ln this study, 12 maize populations were improved with the improved S1 selection method, aiming to increase the improvement efficiency of maize popula-tions. The results showed that number of rows per spike, number of grains per row and 100-grain weight were the three important component traits of maize yield. The highest genetic increment was found in Mengqun 2, fol owed by Mengqun 4, Mengqun 1 and other 7 maize populations. Negative genetic increment was shown in Mengqun 3 and 3 introduced foreign maize populations. Some changes were shown in spikes, plant traits and genetic diversity of maize populations. Based on our results, we concluded that Mengqun 2, Mengqun 4, Zhongzong 7, Mengqun C and Mengqun A could be directly used for the line breeding by selfing for their higher genetic increment of yield and better improvement effects of other agricultural traits. Compared with these 5 populations, the improvement potential of other maize populations was limited for their lower genetic increment.

Method and standard for selecting elite nut trees of Korean pine

The fruit-bearing quantities of nut Korean pines (Pinus Koraiensis) of natural stands in Changbai Mountain, Xiaoxing'an Mountain, and Wanda Mountain and of artificial forest in Hegang area of Heilongjiang Province were investigated and measured by seed collection of singletree during 1988–1998. In order to evaluate the elite nut tree of fructification, the characteristics of fructification of Korena pine, including, the fruit-bearing quantity, output of seed, quantity of cone, cone size, seed size, the ratio of null seed by solid seed, seed percentage of cone, rate of the cones infested with pest, and fruit-bearing index, etc., were analyzed with the variance analysis, multiple comparison and stepwise regression to obtain the contribution ratio of each fruit-bearing factor to fruit-bearing quantity. The multiple correlation factors and the partial correlation factors for fruit-bearing quantities of Korean pine were determined for different geographical areas, and the cone length, thousand-grain-weight, and the seed percentage of cone were considered as important indices for selection of elite trees. The method of modified weighted coefficients was adopted to select the elite nut trees of Korean pine. Standards for selecting elite nut trees from the natural stands and artificial forest of Korean pine were established. This study could provde selection method and standard of elite nut trees for setting up seed orchard of Korean Pine.

Selection Method of Multi-Objective Problems Using Genetic Algorithm in Motion Plan of AUV

To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as an object. A changing weight value method is put forward and a selection formula is modified. Some experiments were implemented on an AUV, TwinBurger. The results shows that this method is effective and feasible.

Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method

One of the most critical and complicated steps in mine design is a selection of suitable mining method based upon geological,geotechnical,geographical,safety and economical parameters.The aim of this study is developing a Monte Carlo simulation to selection the optimum mining method by using effective and major criteria and at the same time,taking subjective judgments of decision makers into consideration.Proposed approach is based on the combination of Monte Carlo simulation with conventional Analytic Hierarchy Process(AHP).Monte Carlo simulation is used to determine the confdence level of each alternative’s score,is calculated by AHP,with the respect to the variance of decision makers’opinion.The proposed method is applied for Jajarm Bauxite Mine in Iran and eventually the most appropriate mining methods for this mine are ranked.

Optimum mining method selection using fuzzy analytical hierarchy process–Qapiliq salt mine,Iran

Mining method selection is the first and the most critical problem in mine design and depends on some parameters such as geotechnical and geological features and economic and geographic factors. In this paper, the factors affecting mining method selection are determined. These factors include shape, thick- ness, depth, slope, RMR and RSS of the orebody, RMR and RSS of the hanging wall and footwall. Then, the priorities of these factors are calculated. In order to calculate the priorities of factors and select the best mining method for Qapiliq salt mine, Iran, based on these priorities, fuzzy analytical hierarchy process （AHP） technique is used. For this purpose, a questionnaire was prepared and was given to the associated experts. Finally, after a comparison carried out based on the effective factors, between the four mining methods including area mining, room and pillar, cut and fill and stope and pillar methods, the stope and nillar mining method was selected as the most suitable method to this mine.

Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

Several millions of people suffer from Parkinson’s disease globally.Parkinson’s affects about 1%of people over 60 and its symptoms increase with age.The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech signals.With the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this data.Several classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many fields.This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based.The dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 patients.The experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.

Selection Method of Production Enterprises by Large Pharmaceutical Commercial Companies Based on AHP

Objective To study the policies of integrating medical resources and centralized drug procurement in China from 2018 to 2020,and to provide a reference for large pharmaceutical commercial companies to select partners.Methods Analytic hierarchy process(AHP)and fuzzy synthesis evaluation method were used to establish the index evaluation system and assign values to each index.Results and Conclusion According to the questionnaire survey data,the weight of each evaluation index was determined,and the evaluation results were obtained by using the fuzzy synthesis evaluation method.The selection of production enterprises by large pharmaceutical commercial companies includes five first-level indicators and 11 second-level indicators.They can provide a favorable reference for the selection of production enterprises by large pharmaceutical commercial companies against the background of complex pharmaceutical industry.

Selection of surgical methods for thoracic ossification of ligamentum flavum combined with cervical spondylotic myelopathy

Objective To investigate the difference between different surgical methods for thoracic ossification of ligamentum flavum(OLF) combined with cervical spondylotic myelopathy(CSM) . Methods From January 1991 to January 2003,56 cases

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System(CPS)applications.Edge devices enable limited computational capacity and energy availability that hamper end user performance.We designed a novel performance measurement index to gauge a device’s resource capacity.This examination addresses the offloading mechanism issues,where the end user(EU)offloads a part of its workload to a nearby edge server(ES).Sometimes,the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources(such as storage and computation).The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services.In this regard,an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions.Consequently,the development of a reinforcement learning-based flexible x-scheduling(RFXS)approach resolves the service offloading issues,where x=service/resource for producing the low latency and high performance of the network.Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency.A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set ofmeasurements,as well as the behavioural association rate and adulation factor.Our system managed an average 0.89%of the service offloading rate,with 39 ms of delay over complex scenarios(using three servers with a 50%service arrival rate).The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty,and is suitable for simulating heterogeneous CPS frameworks.

Conceptual design of oil palm fibre reinforced polymer hybrid composite automotive crash box using integrated approach

A hybrid conceptual design approach was introduced in this study to develop a conceptual design of oil palm polymer composite automotive crash box(ACB). A combination of theory of inventive problem solving(TRIZ), morphological charts and biomimetics was applied where the foremost requirements in terms of the material characteristics, function specifications, force identification, root cause analysis, geometry profile and design selection criteria were considered. The strategy was to use creations of nature to inspire five innovative conceptual designs of the ACB structure and the AHP method was applied to perform the pairwise analysis of selecting the best ACB conceptual design. A new conceptual design for a composite ACB was conceived bearing in mind the properties of natural fibre, unlike those of conventional materials such as steel alloys and aluminium alloys. The design with the highest ranking(26.6 %) was chosen as the final conceptual design, which was the one with a honeycomb structure for the outermost profile, reinforced with a spider web structure inside the part, supported by fibre foam structure extracted from the woodpecker sponge tissue at the centre to maximize the energy absorption capability. The new design could solve the problem of bending collapse which is a major cause of failure to absorb maximum impact energy for ACB during collision. However, the final conceptual design will still need several modifications for production and assembly purposes, which will be completed in a further study.

Reliability Sensitivity-based Correlation Coefficient Calculation in Structural Reliability Analysis

The correlation coefficients of random variables of mechanical structures are generally chosen with experience or even ignored,which cannot actually reflect the effects of parameter uncertainties on reliability.To discuss the selection problem of the correlation coefficients from the reliability-based sensitivity point of view,the theory principle of the problem is established based on the results of the reliability sensitivity,and the criterion of correlation among random variables is shown.The values of the correlation coefficients are obtained according to the proposed principle and the reliability sensitivity problem is discussed.Numerical studies have shown the following results：（1） If the sensitivity value of correlation coefficient ρ is less than（at what magnitude 0.000 01）,then the correlation could be ignored,which could simplify the procedure without introducing additional error.（2） However,as the difference between ρs,that is the most sensitive to the reliability,and ρR,that is with the smallest reliability,is less than 0.001,ρs is suggested to model the dependency of random variables.This could ensure the robust quality of system without the loss of safety requirement.（3） In the case of ｜Eabs｜ρ0.001 and also ｜Erel｜ρ0.001,ρR should be employed to quantify the correlation among random variables in order to ensure the accuracy of reliability analysis.Application of the proposed approach could provide a practical routine for mechanical design and manufactory to study the reliability and reliability-based sensitivity of basic design variables in mechanical reliability analysis and design.

DEVELOPMENT OF CLINICAL STUDIES ON ACUPUNCTURE TREATMENT OF SHOULDER PAIN IN PATIENTS WITH WIND STROKE

In the present paper, the authors review some new results of acupuncture treatment of shoulder pain in stroke patients from ① application of filiform needle with different needling manipulations, ② selection and combination of acupoints, and ③ some special needling methods including point-injection. Generally, if acupuncture therapy is used in combination with other rehabilitation methods, the therapeutic effect of shoulder-pain-relief will be much better. Regarding the acupoint selection, both local acupoints and those of the yang meridians are primarily used in clinical practice. Further studies on treating shoulder pain of hemiplegia based upon the basic theory of TCM are necessary.

Detection of Campylobacter sp. from Poultry Feces in Ouagadougou, Burkina Faso

Background: Campylobacter contamination in poultry and poultry product has been reported worldwide. The present study aims to determine the prevalence of Campylobacter in poultry feces using selective enrichment Bolton broth and multiplex PCR. Method: Two methods were used in this study: the first was direct plating of poultry feces into mCCDA agar plates. The second, three antibiotics were used at different concentrations to add in Bolton broth supplemented. These antibiotics were Rifampicin (Oxoid, Nepean, Ontario) with 10 mg/L, colistin (Oxoid, Nepean, Ontario) with 1 mg/mL and 2 mg/mL;trimethoprim (Oxoid, Nepean, Ontario) with 10 mg/L. The colonies with typical campylobacter morphology on blood agar (little, red and ring colonies) were further identified to the species level by multiplex polymerase chain reaction (PCR). Results: The addition of colistin (2 mg/mL) to the Bolton broth with selective supplements enhanced the selective isolation of Campylobacter strains. Out of the 52 feces samples, 18 (34.61%) were positive for campylobacter and direct plating on mCCDA 11 (21.15%) campylobacter strains (p < 0.05). The PCR results have shown that 17 (94.45%) of the campylobacter strains detected belonged to Campylobacter coli and 1 (5.55%) strain to Campylobacter jejuni. Conclusion: Although it is known to be difficult to isolate Campylobacter from animal feces samples, this study shows that antibiotic selective pressure improves the isolation efficiency of Campylobacter from poultry feces.

Six end-stage renal disease patients benefited from first non-simultaneous single center 6-way kidney exchange transplantation in India

AIM To avoid desensitization protocols and ABO incompatible kidney transplantation(KT) due to high costs and increased risk of infections from intense immunosuppression.METHODS We present institutional ethical review board- approved study of single center 6-way kidney exchange transplantation. The participants comprised ABO incompatibility(n = 1); positive cross-match and/or presence of donor specific antibody(n = 5). The average time required from registration in kidney paired donation(KPD) registry to find suitable donors was 45 d and time required to perform transplants after legal permission was 2 mo. RESULTS Graft and patient survival were 100%, and 100%, respectively. One patient had biopsy-proven acute borderline T cell rejection(Banff update 2013, type 3). Mean serum creatinine was 0.8 mg/dL at 9 mo followup. The waiting time in KPD was short as compared to deceased donor KT. CONCLUSION We report first non-simultaneous, single center, 6-way kidney exchange transplantation from India. Our experience will encourage other centers in India to undertake this practice.

Effect of twine thickness on size-selectivity of driftnet for the yellow croaker Larimichthys polyactis in southwestern Sea of Korea

In order to determine the effect of twine thickness on the size-selectivity of the driftnet used for the yellow croaker, size-selectivity tests were conducted with three different twine thicknesses(monofi lament diameters of 0.279 mm(number's method; No. 3), 0.321 mm(No. 4), and 0.360 mm(No. 5)) of driftnets for the yellow croaker in the seas around Chooja-do, Jeju Islands. The selectivity curve was estimated by using Kitahara's method. In order to determine the physical properties of the twine used in the experimental fi shing nets, we measured the breaking load, elongation, and stiffness under both dry and wet conditions. In terms of physical properties, the thinnest twine(No. 3) had the strongest breaking strength per unit cross-sectional area, along with good elongation and excellent fl exibility. The thickest twine(No. 5) had the lowest fl exibility. In terms of selectivity, the net of No. 3 twine showed the broadest selection range and, thus, a relatively low selectivity compared with the other nets, while the less fl exible net of No. 5 twine showed the narrowest selectivity range and high selectivity. In addition, it was found that a thicker twine resulted in a smaller haul of small fi sh. Therefore, it can be inferred that the thickness of the twine affects the size of the catch and selectivity, and thus the size composition of the catch as well.

Extremum selection method of random variable for nonlinear dynamic reliability analysis of turbine blade deformation

To effectively select random variable in nonlinear dynamic reliability analysis,the extremum selection method(ESM)is proposed.Firstly,the basic idea was introduced and the mathematical model was established for the ESM.The nonlinear dynamic reliability analysis of turbine blade radial deformation was taken as an example to verify the ESM.The results show that the analysis precision of the ESM is 99.972%,which is almost kept consistent with that of the Monte Carlo method;moreover,the computing time of the ESM is shorter than that of the traditional method.Hence,it is demonstrated that the ESM is able to save calculation time and improve the computational efficiency while keeping the calculation precision for nonlinear dynamic reliability analysis.The present study provides a method to enhance the nonlinear dynamic reliability analysis in selecting the random variables and offers a way to design structure and machine in future work.

Establishment of a selective evaluation method for DPP4 inhibitors based on recombinant human DPP8 and DPP9 proteins

Dipeptidyl peptidase 4(DPP4)is recognised as an attractive anti-diabetic drug target,and several DPP4 inhibitors are already on the market.As members of the same gene family,dipeptidyl peptidase 8(DPP8)and dipeptidyl peptidase 9(DPP9)share high sequence and structural homology as well as functional activity with DPP4.However,the inhibition of their activities was reported to cause severe toxicities.Thus,the development of DPP4 inhibitors that do not have DPP8 and DPP9 inhibitory activity is critical for safe anti-diabetic therapy.To achieve this goal,we established a selective evaluation method for DPP4 inhibitors based on recombinant human DPP8 and DPP9 proteins expressed by Rosetta cells.In this method,we used purified recombinant 120 kDa DPP8 or DPP9 protein from the Rosetta expression system.The optimum concentrations of the recombinant DPP8 and DPP9 proteins were 30 ng/mL and 20 ng/mL,respectively,and the corresponding concentrations of their substrates were both 0.2 mmol/L.This method was highly reproducible and reliable for the evaluation of the DPP8 and DPP9 selectivity for DPP4 inhibitor candidates,which would provide valuable guidance in the development of safe DPP4 inhibitors.

Mango internal defect detection based on optimal wavelength selection method using NIR spectroscopy

A non-destructive technique should be developed for performance analysis of mango fruits because the spongy tissue or internal defects could lower the quality of mango fruit and incur a lack of productivity.In this study,wavelength selection methods were proposed to identify the range of wavelengths for the classification of defected and healthy mango fruits.Feature selection methods were adopted here to achieve a significant selection of wavelengths.To measure the goodness of themodel,the datasetwas collected using the NIR(Near Infrared)spectroscopy with wavelength ranging from 673 nm–1900 nm.The classification was performed using Euclidean distance measure both in the original feature space and in FLD(Fisher's Linear Discriminant)transformed space.The experimental results showed that the lower range wavelength(673 nm–1100 nm)was the efficient wavelength for the detection of internal defects in mangoes.Further to express the effectiveness of the model,different feature selection techniques were investigated and found that the Fisher's criterion based technique appeared to be the best method for effective wavelength selection useful for classification of defected and healthy mango fruits.The optimal wavelengths were found in the range of 702.72 nm to 752.34 nm using Fisher's criterionwith a classification accuracy of 84.5%.This study showed that NIR systemis a useful technology for the automaticmango fruit assessmentwhich has the potential to be used for internal defects in online sorting,easily distinguishable by those who do not meet minimum quality requirements.