Effective Hybrid Teaching-learning-based Optimization Algorithm for Balancing Two-sided Assembly Lines with Multiple Constraints

Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

A Teaching Case of Subject Clause Based on Implicit Learning

The New English curriculum criteria suggest teaching English grammar based on the students’cognitive characteristics and emotional needs,helping them discover the rules and encouraging them to master the grammar by using it.But due to the limited time in a lesson,many English teachers adopt a simple approach to teach grammar,in which students are required to memorize the rules first and then practice a lot.This approach is effec-

Task-based Teaching and Learning in English Listening Class

In technical college English listening class,task-based teaching and learning method can not only create harmonious environment for students' learning,but also motivate students' enthusiasm in listening class,thus students can benefit a great deal in listening class and the listening can be carried out successfully.

The MOOC/SPOC Based"1+M+N"Multi-University Collaborative Teaching and Learning Mode:Practice and Experience

Since 2012, the MOOCs, the massive open online courses, have brought big influences on the higher education in the world. How to use MOOCs to help universities rather than bother them to improve their education level and quality becomes an important issue. In China, many universities have explored the new modes and approaches for MOOC/SPOC-based teaching and learning. Especially, the China MOOC Association on Computing Education(CMOOC association), established in 2014, has done a set of successful practice and achieved fruitful experiences on MOOC courses development and computer education reform. Based on the practical experiences, a MOOC/SPOC based "1+M+N" multi-university collaborative teaching and learning mode is presented, which is adapted to the real situation of Chinese university education. In the paper, the practices and experiences of CMOOC association are introduced, the MOOC/SPOC based "1+M+N" multi-university collaborative teaching and learning mode and its approaches are described. Finally, the suggestions for MOOCs development and applications are also presented.

Exploring the Application Effect of Flipped Classroom Combined with Problem-Based Learning Teaching Method in Clinical Skills Teaching of Standardized Training for Resident Doctors of Traditional Chinese Medicine

Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.

A Case Study of a Technology-Based Phonics Teaching and Learning in an Elementary School

Different from International Phonetic Alphabet learning,phonics as an effective way of spelling and reading gets more at⁃tention in China.But it faces many problems in implementation process.This paper introduces a case study of a technology-based phonics teaching and learning.This paper results from two classes in an elementary school revealed that pupils broke through the difficulties of learning phonics on technology-based learning.

Analysis on Output Teaching Mode of College English Based on Implicit Learning Theory

The application of language,to a great extant,requires learners to understand the inputted information quickly as well as automatically,and combine verbal fragments into meaningful outputted language. This type of spontaneous mechanism depends on the effective input of language and long-rang internalization of language structure,which helps to form the implicit knowledge in students' conceptual system,thus to realize the automatic use of language. Therefore,the article intends to combine implicit learning theory with the output teaching mode with a purpose of working out a practical teaching mode to enhance the teaching effect and college students' applied abilities to use English.

An Empirical Study of Legal English Translation Teaching Through Wechat-based Mobile Learning Platform

This paper illustrates the functions of smartphone-based teaching using the theory of constructivism,and puts forward anew learning strategy to replace traditional cram-teaching methods.We examines the new paradigm in the formation of translationcompetence within the legal discourse.It aims to promote the autonomous learning,monitor the students’participation,facilitatestudents’communication and provide well-structured materials to transform traditional classroom learning into mobile phonelearning,to maximize students’initiative and enthusiasm,as well as help students engage in,interpret,and negotiate the complexi-ties that surround them.The findings of this study have been summarized into a few generalizations for possible directions for trans-lation research and they provide a better understanding of Chinese students’translation competence within a legal English contextand contribute to the translation skill development.Series on existing research on translation competence development in classroomteaching contexts for empirical guidance,as an essential component of ESP curriculum based on authentic data and analyzedthrough online framework specifically designed for legal discourse.

Autonomous and Incidental Learning of Vocabulary Through Task-based Language Teaching

This study mainly discussed the effects of three tasks of translating authentic business report on L2 vocabulary learning.160 students were chosen from different majors by a pre-task proficiency test.The findings revealed that task 3 was the optimum task in vo-cabulary gain and direct vocabulary learning had a more facilitated power than incidental vocabulary learning in this translation task forthe learners with the lowest level of vocabulary.This study also suggested that the caution of need and evaluation needed to be adjustedand paid for the learners with the lowest vocabulary level.

TBL(Team-based learning)教学法在局解教学中的设计与评价

为适应现代医学发展的要求,在日益增多的医学教学改革尝试中,TBL教学法引起人们的关注。本文通过在局部解剖学教学中开展TBL教学,并且和传统教学方法做了对比研究。结果提示在局部解剖学教学中采用TBL教学法有利于提高学生学习兴趣及解决问题的能力,有利于动态评价学生的学习状态。

How a Computer Assists English Teaching and Learning among Vocational Colleges

"College English Curriculum Requirements", edited by Department of Higher Education(2007), put forward clearly one of the key points of the national College English teaching reform was to strengthen the application of computer to college English teaching and apply computer-and-classroom-based English teaching mode, improving the previous mode dominated by a single teacher. Most colleges and universities in China have basically achieved the popularity of computer multimedia classrooms and campus networks. However, according to researches(Xia, 2002), most teachers still hold the main role of them in classes as"language interpreter"and"language instructor". Although advanced computer technology has been provided, most teachers feel confused or difficult in using it to assist their English teaching efficiently. As a consequence, computer technology fail to play its role in English classes. Driven by the great development of science and technology, computer has brought about incredible changes in every aspect of social life since 1980 s. In current times, almost every aspect of college students' life has been closely associated with computer. However, in most situations, computer is not taken as a typical language learning tool in their daily life. It is known that most students' English basis is relatively weak in vocational colleges; meanwhile, the way in which they learned English during the middle school period was basically translation- based teaching. Thus they have little or even no interest in English learning at all. In this way, discovering a new and interesting way with the aid of computer to learn English is of essential importance. Based on this, the paper discusses five major aspects under the circumstance of computer-assisted English learning. It is hoped that vocational college English teaching and learning can become more efficient by means of computer technology, finally students' English learning motivation and English competence can be enhanced to a great extent.

Hyperparameter Tuning for Deep Neural Networks Based Optimization Algorithm

For training the present Neural Network(NN)models,the standard technique is to utilize decaying Learning Rates(LR).While the majority of these techniques commence with a large LR,they will decay multiple times over time.Decaying has been proved to enhance generalization as well as optimization.Other parameters,such as the network’s size,the number of hidden layers,drop-outs to avoid overfitting,batch size,and so on,are solely based on heuristics.This work has proposed Adaptive Teaching Learning Based(ATLB)Heuristic to identify the optimal hyperparameters for diverse networks.Here we consider three architec-tures Recurrent Neural Networks(RNN),Long Short Term Memory(LSTM),Bidirectional Long Short Term Memory(BiLSTM)of Deep Neural Networks for classification.The evaluation of the proposed ATLB is done through the various learning rate schedulers Cyclical Learning Rate(CLR),Hyperbolic Tangent Decay(HTD),and Toggle between Hyperbolic Tangent Decay and Triangular mode with Restarts(T-HTR)techniques.Experimental results have shown the performance improvement on the 20Newsgroup,Reuters Newswire and IMDB dataset.

An Empirical Study on Classroom Learning Anxiety of Secondary Vocational English Learners in Content-based Instruction

This research aims to study the relationship between content-based instruction and secondary vocational English learners.Two classes in one secondary vocational school were chosen as participants.The result shows that CBI teaching has a negative correlation with English learning anxiety and has an impact on alleviating students' anxiety.

Physics-Based Active Learning for Design Space Exploration and Surrogate Construction for Multiparametric Optimization

The sampling of the training data is a bottleneck in the development of artificial intelligence(AI)models due to the processing of huge amounts of data or to the difficulty of access to the data in industrial practices.Active learning(AL)approaches are useful in such a context since they maximize the performance of the trained model while minimizing the number of training samples.Such smart sampling methodologies iteratively sample the points that should be labeled and added to the training set based on their informativeness and pertinence.To judge the relevance of a data instance,query rules are defined.In this paper,we propose an AL methodology based on a physics-based query rule.Given some industrial objectives from the physical process where the AI model is implied in,the physics-based AL approach iteratively converges to the data instances fulfilling those objectives while sampling training points.Therefore,the trained surrogate model is accurate where the potentially interesting data instances from the industrial point of view are,while coarse everywhere else where the data instances are of no interest in the industrial context studied.

An Overview of Recently Developed Coupled Simulation Optimization Approaches for Reliability Based Minimum Cost Design of Water Retaining Structures

This paper reviews several recently-developed techniques for the minimum-cost optimal design of water-retaining structures (WRSs), which integrate the effects of seepage. These include the incorporation of uncertainty in heterogeneous soil parameter estimates and quantification of reliability. This review is limited to methods based on coupled simulation-optimization (S-O) models. In this context, the design of WRSs is mainly affected by hydraulic design variables such as seepage quantities, which are difficult to determine from closed-form solutions or approximation theories. An S-O model is built by integrating numerical seepage modeling responses to an optimization algorithm based on efficient surrogate models. The surrogate models (meta-models) are trained on simulated data obtained from finite element numerical code solutions. The proposed methodology is applied using several machine learning techniques and optimization solvers to optimize the design of WRS by incorporating different design variables and boundary conditions. Additionally, the effects of several scenarios of flow domain hydraulic conductivity are integrated into the S-O model. Also, reliability based optimum design concepts are incorporated in the S-O model to quantify uncertainty in seepage quantities due to uncertainty in hydraulic conductivity estimates. We can conclude that the S-O model can efficiently optimize WRS designs. The ANN, SVM, and GPR machine learning technique-based surrogate models are efficiently and expeditiously incorporated into the S-O models to imitate the numerical responses of simulations of various problems.

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Arabic is one of the most spoken languages across the globe.However,there are fewer studies concerning Sentiment Analysis(SA)in Arabic.In recent years,the detected sentiments and emotions expressed in tweets have received significant interest.The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language.Two common models are available:Machine Learning and lexicon-based approaches to address emotion classification problems.With this motivation,the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification(TLBOML-ERC)model for Sentiment Analysis on tweets made in the Arabic language.The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets.To attain this,the proposed TLBOMLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding process.In addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in Arabic tweets.To improve the efficacy of the DAE model,the Teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the parameters.The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset.The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.

Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent days.ABSA refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided text.Most of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as lexicons.In literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and tools.These sources were used for processing and analyzing the Arabic language content like lexicons.However,an important challenge in this domain is the unavailability of sufficient and reliable resources.In this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)technique.The aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic language.In the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful format.Besides,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of sentiments.Furthermore,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM model.This scenario establishes the novelty of current study.The performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

Ensemble Based Learning with Accurate Motion Contrast Detection

Recent developments in computer vision applications have enabled detection of significant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization(SPSOM)and Incremental Deep Convolution Neural Networks(IDCNN)for detecting multiple objects.However,the study considered opticalflows resulting in assessing motion contrasts.Existing methods have issue with accuracy and error rates in motion contrast detection.Hence,the overall object detection performance is reduced significantly.Thus,consideration of object motions in videos efficiently is a critical issue to be solved.To overcome the above mentioned problems,this research work proposes a method involving ensemble approaches to and detect objects efficiently from video streams.This work uses a system modeled on swarm optimization and ensemble learning called Spatiotemporal Glowworm Swarm Optimization Model(SGSOM)for detecting multiple significant objects.A steady quality in motion contrasts is maintained in this work by using Chebyshev distance matrix.The proposed system achieves global optimization in its multiple object detection by exploiting spatial/temporal cues and local constraints.Its experimental results show that the proposed system scores 4.8%in Mean Absolute Error(MAE)while achieving 86%in accuracy,81.5%in precision,85%in recall and 81.6%in F-measure and thus proving its utility in detecting multiple objects.

Online Case Based Self-Study Modules as an Adjunct Learning Tool in Otorhinolaryngology: A Pilot Study

Background/Need for innovation: Undergraduate students in Otolaryngology are on the lookout for easy modes of learning which can help them understand concepts better as well as score more in examinations. A need was hence felt to introduce a new learning resource to supplement traditional teaching-learning methods. Methods: Digital, case based self–study modules were prepared using all open source technology and validated by experts in the specialty. The modules were uploaded on a website specifically created for the purpose. They were pilot tested on twenty consenting third year undergraduate (MBBS) students using a crossover design. Post test comprising of multiple choice questions was administered to the students after a period of two weeks. Feedback was obtained from faculty and students. Results: Test scores were found to be significantly higher amongst students who used the learning modules as a supplement to regular bedside teaching (p < 0.001;Wilcoxon signed rank test). Majority of students agreed that the modules helped them gain confidence during internal assessment examinations and would help revision. Conclusions: Online, case based, self-study modules helped students to perform better when used as a supplement to traditional teaching methods. Students agreed that it enabled easy understanding of subject and helped them gain confidence.

Implications of Task-based Teaching Approach for College English Teaching

Under the traditional mode of college English teaching, students are passive recipients of information in class, while teachers dominate the class most of time. Under such circumstances, grammar-translation approach and audio-lingual approach are mainly applied in teaching. However, the application of task-based teaching approach in college English teaching makes a big difference. The students are no longer the passive recipients of information but the active participants. The advantages of this approach are well embodied in various aspects of college English teaching, including reading, writing, listening and speaking.