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In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

This study aims to examine how teacher candidates (TCs) in Türkiye and the USA navigate their intercultural communication skills in a telecollaboration project.

Forty-eight TCs participated (26 in Türkiye and 22 in the USA) in the study. TCs discussed critical issues in multicultural education on an online learning platform for six weeks. Their discussions were analyzed using content and discourse analysis.

The findings indicated that TCs approached the telecollaborative space as a translingual contact zone and positioned themselves and their interlocutors in the discourse by using the personal pronouns; I, we, you and they. When they positioned themselves using we (people in Türkiye/USA), they spoke on behalf of everyone included in the scope of we. Their interlocutors responded to these positionings either by accepting this positioning and responding with a parallel positioning or by engaging in translingual negotiation strategies to revise the scope of we and sharing some differences/nuances in beliefs and practices in their community.

When TCs talk about their culture and community in a singular manner using we, they frame them as the same across every member in that community. When they ask questions to each other using you, the framing of the questions prime the respondents to sometimes relay their own specific experiences as the norm or consider experiences from different points of view through translingual negotiation strategies. A singular approach to culture(s) may affect the marginalized communities the most because they are lost in this representation, and their experiences and voices are not integrated in the narratives or integrated with stereotypical representation.

Teachers and teacher educators should first pay attention to their language choices, especially use of pronouns, which may communicate inclusion or exclusion in intercultural conversations. Next, they should prepare their students to adopt and practice language choices that communicate respect for cultural diversity and are inclusive of marginalized populations.

Speakers’ pronoun use includes identity construction in discourse by drawing borders around and between communities and cultures with generalization and particularity, and by patrolling those borders to decide who is included and excluded. As a response, interlocutors use pronouns either to acknowledge those borders and respond with corresponding ones from their own context or negotiate alternative representations or further investigate for particularity or complexity. In short, pronouns could lead the direction of intercultural conversations toward criticality and complexity or otherwise, and might be reasons where there are breakdowns in communication or to fix those breakdowns.

This study shows that translingual negotiation strategies have explanatory power to examine how speakers from different language backgrounds negotiate second and third order positionings in the telecollaborative space.

Different external supply scenarios faced by the retailers will affect their choice of strategy when supply is disrupted and becomes far less than demand, urgently. This study focuses on analyzing both demand and supply side response strategies to meet customer demand and reduce the impact of the shortage during supply disruptions.

According to the quantity of products that the external market can provide, the external supply scenarios were divided into sufficient-type external supply and learning-type external supply. A two-echelon perishable goods supply chain was analyzed, and three kinds of contingency strategy models for downstream retailers were investigated. First, in the sufficient external supply scenario, the optimal price and transshipment quantity to maximize retailer's profits is discussed. Second, in the scenario of learning-type external supply, this study analyzes the optimal decision in three mechanisms of the hybrid strategy and their application: price priority mechanism, quantity priority mechanism and price–quantity balance mechanism. Furthermore, the influence of penalty cost and supply on the priority orders of different mechanisms was studied.

Results show that comparing the two pure strategies (pricing strategy and transshipment strategy)it was noted that the hybrid strategy produces the best results in sufficient-type external supply scenario. In the learning-type external supply scenario, a numerical study has shown the existence of three areas in case of penalty cost and supplier's capacity, and each areas has different priority orders of the three mechanisms. Under the situation of learning external supply, the retailer's optimal strategy is affected by parameters such as penalty cost and supply volume.

The main innovation of the work lies in the following: First; the external supply situation was divided into sufficiency type and learning type, which improves the external situation faced by retailers after the outbreak of emergencies, helps retailers understand the external situation, conforms to the actual situation and has certain practical application value. Second; in the context of learning external supply, there are three coping strategies for retailers, including: Price priority mechanism, Quantity priority mechanism and Pricing and transshipment balance mechanism. This will help retailers make strategic choices, make more scientific management decisions and improve the supply chain emergency management theory. Third; the demand side response was managed through the change of external supply during supply side recovery period and supply disruption. The proposed model enables managing and analyzing supply disruption efficiently and effectively via handling uncertainty by considering all aspects of decision-making process. The proposed model can be applied in various fields such as vegetable and fruit, fresh food, etc.

The theory of third order inference is a theory of how cultural beliefs influence individuals' decisions under conditions of interdependence and uncertainty. In this study, I build on prior work extending the theory to the role of third order information on social trust in public goods dilemmas. Namely, I argue that when second order information on the beliefs of those relevant to the group task are present, this information should influence decision-making over first and third order.

I test this argument in an experimental public goods game. After measuring first order social trust, participants are randomly sorted into one of four conditions – two that pair third and second order information on social trust as parallel and two that pair them as in conflict.

The results suggest that in the presence of second order information on social trust, third order information doesn't have an effect on cooperation.

The study extends the theory of third order inference to understanding the role of social trust at the first, second, and third levels in public goods dilemmas. It puts second order information in competition with third order in predicting cooperation. It suggests that resolving the uncertainty over the second order beliefs of a collective is key to preventing inefficient equilibriums when second and third order beliefs conflict.

The paper aims to contributes on the debates about University Idea Incubation by investigating the role and the engagement of different University's stakeholders in the process of opportunity recognition in an entrepreneurship education program targeted at students with an interdisciplinary background.

Through a longitudinal case study methodology, the Contamination Lab at University of Salento (Lecce, Italy), the learning approaches and the knowledge process to create an entrepreneurial awareness, mindset and capability in students with different educational background are presented.

The findings demonstrates the crucial role of stakeholders' engagement for business idea presentation, open innovation challenge, contamination workshop on specialized topics, enterprise projects are important vehicle for effective students' business ideas and innovative projects development in a multidisciplinary environment. The close interaction among students, academia, companies and institutions creates a favourable environment that enables opportunity identification, idea generation through a deep contamination of knowledge, skills and experiences.

Limitations include the need to generalise the results even if this limitation is typical of the case study methodology. Other research is necessary for an in-depth analysis in deep of the other Contamination Lab in Italy and to derive the “invariance traits” of this environment according to the features of the local entrepreneurial ecosystems.

Implications for practices include recommendations for designing innovative programs where the interactions between University-Institutions-Industry are realized.

A conceptual framework is proposed by defining all the entrepreneurial knowledge process and knowledge creation within the Contamination Lab, highlighting the contribution of the stakeholders in each phase and learning initiative of the program.

The purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more suitable for solving large-scale optimization issues.

Utilizing multiple cooperation mechanisms in teaching and learning processes, an improved TBLO named CTLBO (collectivism teaching-learning-based optimization) is developed. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes. Applying modularization idea, based on the configuration structure of operators of CTLBO, six variants of CTLBO are constructed. For identifying the best configuration, 30 general benchmark functions are tested. Then, three experiments using CEC2020 (2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms. At last, a large-scale industrial engineering problem is taken as the application case.

Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO. Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems. The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c, revealing that CTLBO and its variants can far outperform other algorithms. CTLBO is an excellent algorithm for solving large-scale complex optimization issues.

The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism, self-learning mechanism in teaching and group teaching mechanism. CTLBO has important application value in solving large-scale optimization problems.

The research aims to discuss technical and vocational students' long-term and complete evaluation of learning effectiveness under school-wide thematic teaching design.

This study adopted a mixed qualitative and quantitative approach to measure the learning effectiveness of thematic teaching in technical vocational schools using the Kirkpatrick model. Qualitative research conducted in-depth interviews with 32 interviewees, including students, parents, teachers, graduate alumni and the supervisors of off-campus internship units. Quantitative research conducted a questionnaire survey on vocational students. A total of 221 valid questionnaires were collected. In addition, this research conducts another quantitative survey on cooperative enterprises to compare the actual effect of the implementation of the school-wide thematic teaching students with the others, and a total of 35 valid questionnaires were collected.

The results of the research found that the effectiveness of thematic teaching method can achieve the expected goals of each level of Kirkpatrick model. The students taking thematic teaching are significantly better and fitting in the industry expects. Therefore, this research suggests the comprehensive introduction of school-wide thematic teaching to other school operators.

This research is the first study used the Kirkpatrick model to evaluate the effectiveness of school-wide thematic teaching design in hospitality education and providing a practical case for schools. This research combined qualitative and quantitative research methods to investigate the effectiveness of the teaching method through multiple perspectives. Through the feedback from supervisors of the hospitality industry, the school-wide thematic teaching design provides a good foundation for technical and vocational graduates.

The purpose of this conceptual paper is to demonstrate how memes perpetuate trauma with a schematic. This conceptual paper uses the “begin with the end in mind” meme to format the presentation of the necessary components for the schematic of how trauma persists across generations. It is the third paper in a series of applications of the recursive, test-operate-test schematic to the systemic effects of the information processes involved in trauma. The schematic presented permits evaluations of solutions to interrupt the generational trauma cycle.

The required schematic components are described. Trauma (actual or perceived threat to survival) will be briefly discussed. Evolutionary processes that create the psychophysiology necessary to support nominal social expectations (NSEs) memes and metaphors will be summarized. The development of NSEs will be discussed. Metaphors and memes necessary for the creation of the schematic and esoteric events at level Learning IV will be briefly described. Finally, Figure 3, which illustrates the maintenance of NSEs and attempts to prevent their violation, will be explained.

It is asserted that functional human social behavior requires NSEs. Trauma is found to persist through the presence of anti-nominal NSE memes that are transduced by the individual into anti-nominal metaphors, which then damage grid, place and dentate gyrus cell (GPDG) neurophysiology. The damaged neurocircuits allow the use of anti-nominal NSE metaphors within our individual neurophysiology. Furthermore, anti-NSE memes interfere with the self-organized criticalities (SOCs) of genetic-epigenetic processes necessary for the intergenerational transfer of functional social behavior. When anti-NSE nominal metaphors are transduced back into anti-NSEs, social niche memes, the trauma process is reiterated. Anti-NSE memes and metaphors are found to be inappropriate criteria central to the maintenance of persistent trauma. Therefore, anti-NSE memes have hijacked our epigenetics and our social niches. Solutions are available because during our evolution, the Homo clade developed esoteric capabilities and the ability to bring back what information we can from those encounters. This physiology operates around the 5HT2A neural receptors that process hallucinogens, such as psilocybin. Accessing this resource system, either through naturally occurring altered states of consciousness or through micro-dose pharmaceutical psilocybin and related neurotransmitters, produces a significant structural change in the GPDG system to reset the NSE system illustrated in the schematic to its nominal status so that we can maintain nominal NSE relationships within our meme niche(s).

The source of persistent trauma in our social niche(s), the means by which the trauma is maintained and the means to mitigate and perhaps eliminate persistent trauma are identified. Based on these three conclusions, it is difficult to make decisions regarding corrective actions because of ubiquitous anti-NSE memes and because of the limitations of our ordinary consciousness.

If we wish to survive as a species, we will need to discover the criteria necessary to maintain our niche(s) congruent SOCs and use them instead of tyrannical memes described by Dawkins (1989) to make decisions about ourselves and our niche(s).

Significant courage is required to identify the memes that maintain trauma because many of them are culturally sacred cows. Unfortunately, we have known since Bremner's (1995) MRI study of posttraumatic stress disorder that trauma causes brain damage. Fortunately, our NSE genes compel us to pursue restitution of the memes that maintain our trauma-inducing cultures.

Several original assertions are presented. While the Homo clade ancestors began the creation of the social niche(s) that led to Homo sapiens sapiens, it is asserted that the australopiths created the NSE memes which are the foundation behaviors that permit our social niche(s). Furthermore, NSEs were produced by enhanced intentionality skills and NSEs were created by both genetic and memetic processes. The evolution of intentionality-NSE neural networks is asserted as the source of intentional material manipulation. While anti-NSE memes are claimed as the source of persistent trauma, the practice of esoteric technologies is presented as a solution to persistent trauma.