Application of Heuristic Method to the Clinical Teaching of Internal Medicine

There is an old saying,“Give a man a fish,and he will eat for a day.Teach a man to fish,and he will eat for the rest of his life.”In clinical teaching,students should not only be taught about diseases,but their intelligence should also be cultivated,along with their analytical,comprehension,and independent learning skills.The ability to solve problems enables students to think independently and acquire knowledge.This is known as the heuristic method of teaching.In this study,we mainly analyze the application value of the heuristic method in the clinical teaching of internal medicine.

Algorithms for Multicriteria Scheduling Problems to Minimize Maximum Late Work, Tardy, and Early

This study examines the multicriteria scheduling problem on a single machine to minimize three criteria: the maximum cost function, denoted by maximum late work (Vmax), maximum tardy job, denoted by (Tmax), and maximum earliness (Emax). We propose several algorithms based on types of objectives function to be optimized when dealing with simultaneous minimization problems with and without weight and hierarchical minimization problems. The proposed Algorithm (3) is to find the set of efficient solutions for 1//F (Vmax, Tmax, Emax) and 1//(Vmax + Tmax + Emax). The Local Search Heuristic Methods (Descent Method (DM), Simulated Annealing (SA), Genetic Algorithm (GA), and the Tree Type Heuristics Method (TTHM) are applied to solve all suggested problems. Finally, the experimental results of Algorithm (3) are compared with the results of the Branch and Bound (BAB) method for optimal and Pareto optimal solutions for smaller instance sizes and compared to the Local Search Heuristic Methods for large instance sizes. These results ensure the efficiency of Algorithm (3) in a reasonable time.

Chromatographic fingerprint analysis of Fructus Aurantii Immaturus by HPLC-DAD and chemometric methods

An efficient method for quality control of Fructus Aurantii Immaturus (FAI),a famous traditional Chinese medicine (TCM) was established. A simple and reliable high-performance liquid chromatography-photodiode array detector (HPLC-DAD) procedure coupled with chemometric methods was developed for fingerprint analysis,qualitative analysis and quantitative determination of this herb. In qualitative and quantitative analyses,heuristic evolving latent projection (HELP) method was employed to resolve the overlapping peaks of the tested samples. Two bioactive components,namely hesperidin and naringin,are confirmed and determined,together with four flavonoids compounds tentatively identified including two new ones. From fingerprint analysis,the fingerprint data were processed with correlation coefficients for quantitative expression of their similarity and dissimilarity. The developed method based on an integration of chromatographic fingerprint and quantitative analysis is scientific,and the obtained results can be applied to the quality control of herb medicine.

Comparative study of various methods for extraction of multi-quantum wells Schottky diode parameters

In this work,forward current voltage characteristics for multi-quantum wells Al_(0.33)Ga_(0.67)As Schottky diode were measured at temperature ranges from 100 to 300 K.The main parameters of this Schottky diode,such as the ideality factor,barrier height,series resistance and saturation current,have been extracted using both analytical and heuristics methods.Differential evolution(DE),particle swarm optimization(PSO)and artificial bee colony(ABC)have been chosen as candidate heuristics algorithms,while Cheung technic was selected as analytical extraction method.The obtained results show clearly the high performance of DE algorithms in terms of parameters accuracy,convergence speed and robustness.

On the Computing of the Minimum Distance of Linear Block Codes by Heuristic Methods

The evaluation of the minimum distance of linear block codes remains an open problem in coding theory, and it is not easy to determine its true value by classical methods, for this reason the problem has been solved in the literature with heuristic techniques such as genetic algorithms and local search algorithms. In this paper we propose two approaches to attack the hardness of this problem. The first approach is based on genetic algorithms and it yield to good results comparing to another work based also on genetic algorithms. The second approach is based on a new randomized algorithm which we call 'Multiple Impulse Method (MIM)', where the principle is to search codewords locally around the all-zero codeword perturbed by a minimum level of noise, anticipating that the resultant nearest nonzero codewords will most likely contain the minimum Hamming-weight codeword whose Hamming weight is equal to the minimum distance of the linear code.

Heuristic Method for Job Shop Scheduling Problem with Combination Processing

A job shop scheduling problem with a combination processing in complex production environment is proposed. Based on the defining of ＂non-elastic combination processing relativity＂ and ＂virtual process＂, the problem can be simplified and transformed to a traditional one. On the basis of the dispatching rules select engine and considered factors of complex production environment, a heuristic method is designed. The algorithm has been applied to a mould enterprise in Shenzhen for half a year. The practice showed that by using the method suggested the number of delayed orders was decreased about 20% and the productivity was increased by 10 to 20%.

Study on the tradeoff between interpretability and precision in fuzzy modeling

An approach to identifying fuzzy models considering both interpretability and precision was proposed. Firstly, interpretability issues about fuzzy models were analyzed. Then, a heuristic strategy was used to select input variables by increasing the number of input variables, and the Gustafson-Kessel fuzzy clustering algorithm, combined with the least square method, was used to identify the fuzzy model. Subsequently, an interpretability measure was described by the product of the number of input variables and the number of rules, while precision was weighted by root mean square error, and the selection objective function concerning interpretability and precision was defined. Given the maximum and minimum number of input variables and rules, a set of fuzzy models was constructed. Finally, the optimal fuzzy model was selected by the objective function, and was optimized by a genetic algorithm to achieve a good tradeoff between interpretability and precision. The performance of the proposed method was illustrated by the well-known Box-Jenkins gas furnace benchmark; the results demonstrate its validity.

Adaptive Soft-sensor Modeling Algorithm Based on FCMISVM and Its Application in PX Adsorption Separation Process

To overcome the problem that soft sensor models cannot be updated with the process changes, a soft sensor modeling algorithm based on hybrid fuzzy c-means （FCM） algorithm and incremental support vector machines （ISVM） is proposed. This hybrid algorithm FCMISVM includes three parts： samples clustering based on FCM algorithm, learning algorithm based on ISVM, and heuristic sample displacement method. In the training process, the training samples are first clustered by the FCM algorithm, and then by training each clustering with the SVM algorithm, a sub-model is built to each clustering. In the predicting process, when an incremental sample that represents new operation information is introduced in the model, the fuzzy membership function of the sample to each clustering is first computed by the FCM algorithm. Then, a corresponding SVM sub-model of the clustering with the largest fuzzy membership function is used to predict and perform incremental learning so the model can be updated on-line. An old sample chosen by heuristic sample displacement method is then discarded from the sub-model to control the size of the working set. The proposed method is applied to predict the p-xylene （PX） purity in the adsorption separation process. Simulation results indicate that the proposed method actually increases the model＇s adaptive abilities to various operation conditions and improves its generalization capability.

Debris flow susceptibility and propagation assessment in West Koyulhisar, Turkey

Turkey is highly prone to landslides because of the geological and geographic location.The study area,which is located in a tectonically active region,has been significantly affected by mass movements.Flow type landslides are frequently observed due to this location.This study aims at determining the source area and propagation of debris flows in the study area.We used the heuristic method to extract source areas of debris flow,and then used receiver operating characteristic(ROC)curve analysis to assess the performance of the method,and finally calculated the Area under curve(AUC)values being 83.64%and 80.39%for the success rate and prediction rate,respectively.We calculated potential propagation area and runout distance with Flow-R software.In conclusion,the obtained results(susceptibility map,propagation and runout distance)are very important for decisionmakers at the region located on an active fault zone,which is highly prone to natural disasters.The outputs of this study could be used in site selection studies,designing erosion prevention systems and protecting existing human-made structures.

Flight Schedule Recovery under Uncertain Airport Capacity

Airlines adjust their flight schedules to satisfy more stringent airport capacity constraints caused by inclement weather or other unexpected disruptions.The problem will be more important and complicated if uncertain disruptions occur in hub airports.A two-stage stochastic programming model was established to deal with the realtime flight schedule recovery and passenger re-accommodation problem.The first-stage model represents the flight re-timing and re-fleeting decision in current time period when capacity information is deterministic,while the second-stage recourse model evaluates the passenger delay given the first-stage solutions when one future scenario is realized.Aiming at the large size of the problem and requirement for quick response,an algorithmic framework combining the sample average approximation and heuristic method was proposed.The computational results indicated of that the proposed method could obtain solutions with around 5% optimal gaps,and the computing time was linearly positive to the sample size.

Efficient decomposition-based algorithm to solve long-term pipeline scheduling problem

This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining the model resolution by using linear programming-based methods and commercial solvers would be very time-consuming.In this paper,we make an attempt to utilize the problem structure and develop a decomposition-based algorithm capable of finding near-optimal solutions for large instances in a reasonable time.The algorithm starts with a relaxed version of the model and adds a family of cuts on the fly,so that a near-optimal solution is obtained within a few iterations.The idea behind the cut generation is based on the knowledge of the underlying problem structure.Computational experiments on a real-world data case and some randomly generated instances confirm the efficiency of the proposed algorithm in terms of the solution quality and time.

Quantitative Structure-retention Relationship Study on the GC-MS Retention Time of Illicit Drugs

A quantitative structure-retention relationship（QSRR） study has been carried out on the gas chromatograph-mass spectrometry（GC-MS） system retention time（RT） of two sets of illicit drugs by using molecular structural descriptors.Heuristic method（HM） was utilized to construct the linear models.Appropriate models with low standard errors and high correlation coefficients were obtained（R2=0.9873,F=390.18 for data set 1 and R2=0.9881,F=749.13 for data set 2）.The results of leave-one-out cross validation showed good predictive ability of these proposed models（R c2v= 0.9812 and R c2v= 0.9824,respectively）.Each molecular descriptor in the two models was disputed to unfold the relationship between the molecular structures and RT.

Clustering compression-based computation-efficient calibration method for digital twin modeling of HVAC system

Digital twin is regarded as the next-generation technology for the effective operation of heating,ventilation and air conditioning(HVAC)systems.It is essential to calibrate the digital twin models to match them closely with real physical systems.Conventional real-time calibration methods cannot satisfy such requirements since the computation loads are beyond acceptable tolerances.To address this challenge,this study proposes a clustering compression-based method to enhance the computation efficiency of digital twin model calibration for HVAC systems.This method utilizes clustering algorithms to remove redundant data for achieving data compression.Moreover,a hierarchical multi-stage heuristic model calibration strategy is developed to accelerate the calibration of similar component models.Its basic idea is that once a component model is calibrated by heuristic methods,its optimal solution is utilized to narrow the ranges of parameter probability distributions of similar components.By doing so,the calibration process can be guided,so that fewer iterations would be used.The performance of the proposed method is evaluated using the operational data from an HVAC system in an industrial building.Results show that the proposed clustering compression-based method can reduce computation loads by 97%,compared to the conventional calibration method.And the proposed hierarchical heuristic model calibration strategy is capable of accelerating the calibration process after clustering and saves 14.6%of the time costs.

Residential Revitalization through the Universal Heuristic Evaluation Model （UHEM）

The study aims to propose using a universal heuristic evaluation model （UHEM） to improve the functional and physical performance of residential buildings. Since, ＂everyone should be able to enter and use any part of the built environment as independently and naturally as possible＂, the old buildings should be revitalized with respect to an inclusive approach. However, research on current design practice showed that there is a lack of systematic evaluation and revitalization methods. Hence, the main objective of the proposed UHEM model is to evaluate existing residential environments and requalify them with respect to an inclusive approach. The study concludes by highlighting the importance of UHEM from two points of view： （i） the importance of a systematic evaluation approach to effectively deal with the challenge of requalifying the residential environments and （ii） the designers＇ key role during the revitalization process.

A Bound Heuristic Technique for Solving DRAMA Spares Optimizations

This paper studies the solution technique to solve the DRAMA spares allocation optimization problem. DRAMA model is an analytic spare optimization model of a multi-item, multi-location, and two-echelon inventory system. The computation of its system spares availability is much complicated. The objective function and constraint functions of DRAMA model could be written as the separable forms. A new bound heuristic algorithm has been presented by improving the bound heuristic algorithm for solving the reliability redundancy optimization problem (BHA in short). With the results, the proposed algorithm has been found to be more economical and effective than BHA to obtain the solutions of large DRAMA model. The new algorithm could be used to solve reliability redundancy optimization problems with the separable forms.

A Scenario-Based Robust Transmission Network Expansion Planning Method for Consideration of Wind Power Uncertainties

This paper uses a novel scenario generation method for tackling the uncertainties of wind power in the transmission network expansion planning(TNEP)problem.A heuristic moment matching(HMM)method is first applied to generate the typical scenarios for capturing the stochastic features of wind power,including expectation,standard deviation,skewness,kurtosis,and correlation of multiple wind farms.Then,based on the typical scenarios,a robust TNEP problem is presented and formulated.The solution of the problem is robust against all the scenarios that represent the stochastic features of wind power.Three test systems are used to verify the HMM method and is compared against Taguchi’s Orthogonal Array(OA)method.The simulation results show that the HMM method has better performance than the OA method in terms of the trade-off between robustness and economy.Additionally,the main factors influencing the planning scheme are studied,including the number of scenarios,wind farm capacity,and penalty factors,which provide a reference for system operators choosing parameters.

Determination of a suitable index for a solvent via two-column extractive distillation using a heuristic method

The traditional approach to solvent selction in the extractive distillation process strictly focuses on the change in the relative voltility of light-heavy components induced by the solvent.However,the total annual cost of the process may not be minimal when the solvent induces the largest change in relative volatility.This work presents a heuristic method for selecting the optimal solvent to minimize the total annual cost.The functional relationship between the relative volatility and the total annual cost is established,where the main factors,such as the relative volatility of the light-heavy components and the relative volatility of the heavy-component solvent,are taken into account.Binary azeotropic mixtures of methanol-toluene and methanol-acetone are separated to verify the feasibility of the model.The results show that using the solvent with the minimal two-column extractive distillation index,the process achieves a minimal total annual cost.The method is conducive for sustainable advancements in chemistry and engineering because a suitable solvent can be selected without simulation verification.

Quantitative structure-activity relationship of compounds binding to estrogen receptor β based on heuristic method

Estrogen compounds may pose a serious threat to the health of humans and wildlife. The estrogen receptor （ER） exists as two subtypes, ERα and ERβ. Compounds might have different relative affinities and binding modes for ERα and ERβ. In this study, the heuristic method was performed on 31 compounds binding to ERβ to select 5 variances most related to the activity （LogRBA） from 1524 variances, which were then employed to develop the best model with the significant correlation and the best predictive power （γ^2 = 0.829, q^2LOO = 0.742, γ^2pred = 0.772, q^2ext = 0.724, RMSEE = 0.395） using multiple linear regression （MLR）. The model derived identified critical structural features related to the activity of binding to ERβ. The applicability domain （AD） of the model was assessed by Williams plot.

A two-stage heuristic method for vehicle routing problem with split deliveries and pickups

The vehicle routing problem(VRP) is a well-known combinatorial optimization issue in transportation and logistics network systems. There exist several limitations associated with the traditional VRP. Releasing the restricted conditions of traditional VRP has become a research focus in the past few decades. The vehicle routing problem with split deliveries and pickups(VRPSPDP) is particularly proposed to release the constraints on the visiting times per customer and vehicle capacity, that is, to allow the deliveries and pickups for each customer to be simultaneously split more than once. Few studies have focused on the VRPSPDP problem. In this paper we propose a two-stage heuristic method integrating the initial heuristic algorithm and hybrid heuristic algorithm to study the VRPSPDP problem. To validate the proposed algorithm, Solomon benchmark datasets and extended Solomon benchmark datasets were modified to compare with three other popular algorithms. A total of 18 datasets were used to evaluate the effectiveness of the proposed method. The computational results indicated that the proposed algorithm is superior to these three algorithms for VRPSPDP in terms of total travel cost and average loading rate.

A method for groundwater prospect zonation in data poor areas using remote sensing and GIS:a case study in Kalikavu Panchayath of Malappuram district,Kerala,India

The present study was an attempt to delineate potential groundwater zones in Kalikavu Panchayat of Malappuram district,Kerala,India.The geo-spatial database on geomorphology,landuse,geology,slope and drainage network was generated in a geographic information system(GIS)environment from satellite data,Survey of India topographic sheets and field observations.To understand the movement and occurrence of groundwater,the geology,geomorphology,structural set-up and recharging conditions have to be well understood.In the present study,the potential recharge areas are delineated in terms of geology,geomorphology,land use,slope,drainage pattern,etc.Various thematic data generated were integrated using a heuristic method in the GIS domain to generate maps showing potential groundwater zones.The composite output map scores were reclassified into different zones using a decision rule.The final output map shows different zones of groundwater prospect,viz.,very good(15.57%of the area),good(43.74%),moderate(28.38%)and poor(12.31%).Geomorphic units such as valley plains,valley fills and alluvial terraces were identified as good to excellent prospect zones,while the gently sloping lateritic uplands were identified as good to moderate zones.Steeply sloping hilly terrains underlain by hard rocks were identified as poor groundwater prospect zones.