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The study explores the connection between foresight and Artificial Intelligence (AI) methods in a community within an environment of social instability in Colombia. It aims to contribute to research on aligning these methods for future-shaping, with the goal of enhancing shared governance, peer learning and collective learning among traditional decision-makers and local communities in emerging countries. The study seeks to foster a community of social actors who are likely to engage constructively in strategic dialogues. To enhancing shared governance and learning a hybrid model is synthesized, combining foresight and computational intelligence.

The case study explores the integration of computational intelligence and foresight through Gaston Bachelard's (Bachelard, 1936) phenomenology concept of ante-perception. The mathematical representation of the cone of scenarios provides a structured way to explore multiple future pathways, allowing communities to visualize and compare different trajectories and make informed decisions amid uncertainty. The model facilitates critical reflections on present assumptions, deepening insights into future scenarios. Ante-perception challenges traditional approaches to foresight by encouraging a break from established experiences, allowing for novel insights into possible futures. When enriched by computational intelligence, this reflective process is further strengthened by quantitative approach scenario modeling.

This research develops and tests a proposal that includes the logic and methods for constructing a mathematical representation of the cone of scenarios. This process, which is interactive and deliberative, is driven by anticipation and combines qualitative and quantitative approaches within a context of high uncertainty. By combining the critical reflection facilitated by ante-perception with the predictive power of computational intelligence, the model allows communities to transcend established thought patterns and explore innovative future possibilities. This integrative approach helps them envision and work toward social self-transformation.

The article aims to identify the creation of scenarios in contexts of high uncertainty, to respond to the needs of communities in emerging countries to manage change.

This article explores a novel approach to using foresight for address collective intelligence by developing a shared future vision in high-uncertainty contexts within local communities in emerging countries. The application of the hybrid model demonstrates that foresight is a key innovative social tool for developing long-term strategic reflection and planning for territories.

In developing long-term reflective processes, explaining phenomena, mechanisms and correlations requires the use of value judgments. This set of value judgments requires a representation that facilitates their treatment, helps to account for their behavior during the inference process to form a shared future vision. Consequently, ensuring the recognition of the opinions of local communities through participatory discussion spaces and their subsequent refinement, from a technical perspective, aims to illustrate the development of this social construction process. While similarities exist, differences add value through a transfer process, often subconscious. This process stimulates collective learning and builds capacities as knowledge is developed through inquiry, evaluation, interpretation and generalization.

This research provides a unique hybrid model that fosters collective learning and engagement by integrating local community perspectives with advanced computational intelligence methods. By facilitating both reflective and quantitative approach future-shaping, it offers a practical framework for addressing uncertainty while empowering communities to shape their own futures. It underscores the importance of recognizing local community views through bottom-up participatory discussions, thereby widening the stakeholder community to active engagement in addressing broader societal issues. The case study focuses on community collaboration in Puerto Gaitán, a Colombian municipality.

This paper aims to evaluate the internationalization of countries represented in the MSCI Emerging Markets Index. The evaluation considers high-technology product imports, foreign direct investments, research and development expenditures, patent and industrial design applications, exports and gross domestic product (GDP) data, representing production.

This paper uses the Step-Wise Weight Assessment Ratio Analysis (SWARA) method to assess and rank the criteria for evaluating emerging markets. Four academics who are authorities in the field of foreign trade were consulted to weigh the criteria. The Weighted Aggregated Sum Product Assessment (WASPAS) method, considered one of the most reliable and robust MCDM techniques, was then used to evaluate and rank the countries.

The findings obtained during the research process reveal the internationalization dynamics of these countries based on the indicators they possess. As a result, it is observed which countries achieve higher performance in the internationalization process based on relevant criteria.

This study is theoretically unique and valuable as it simultaneously offers the potential for development to macro-level international trade theories, such as the technologic deficit hypothesis, and meso-level internationalization theories, such as the Uppsala model. Additionally, integrated multiple criteria decision-making (MCDM) methods, which are not frequently used in cross-country studies, have been used, and the performance of emerging markets has been ranked. The findings obtained from this application distinguish the research from the existing literature by transforming it into a performance ranking rather than focusing on the antecedents or consequences of internationalization and their interactions.

This note presents a method of teaching accounting problems involving the use of the effective interest method such as bonds, notes, capital leases, and installment sales. The method is conceptually sound and simpler than the traditional method found in current textbooks and stimulates student interest by focusing on the economics of the transaction and relating it to real-life examples.

To assess its pedagogical efficacy, the method was tested in the introductory and intermediate accounting classes. In both courses, the results indicate that students' test scores are significantly higher under the new method than the traditional method. It is hoped that this evidence of the superiority of the new method in a classroom environment will spur its adoption by instructors and textbook writers.

This study explores the distribution of insufficient effort responders according to methods of classifying students’ evaluation of teaching effectiveness in higher education. Five different methods were found in the literature to classify students’ evaluation of teaching effectiveness in higher education.

Quantitative research methodology was used to achieve the goals of this study. Data from a major public university was used through 20 five-point items that are designed to measure students’ evaluation of teaching effectiveness. The dataset that consisted of 26,679 surveys was analyzed. Detecting insufficient efforts responding was based on item response theory procedures.

The results show that insufficient effort responders are distributed differently to students’ evaluation of teaching effectiveness in higher education levels according to different methods of classifying these levels. The results of this study suggest using a percentage of students’ agreement of 4 or 5 for each item to classify SET levels and deleting IERs before interpreting SET results.

According to the results of this study, it is recommended to research the relationships between IER and SET scores and students’ motivation to participate in evaluating teaching effectiveness.

According to the results of this study, it is recommended to:1– Exclude the IERs from the dataset before generating SET reports. 2– Use the percentage of 4 (agree) and 5 (strongly agree) satisfactions of SET items to classify and interpret SET results.

Reviewing the literature shows the absence of studies that explore the distribution of insufficient effort responders according to methods of classifying students’ evaluation of teaching effectiveness in higher education. The results suggest using a percentage of students’ agreement of 4 or 5 for each item to classify SET levels and deleting IERs before interpreting SET results.

The purpose of this paper is to propose a new combined finite-discrete element method (FDEM) to analyze the mechanical properties, failure behavior and slope stability of soil rock mixtures (SRM), in which the rocks within the SRM model have shape randomness, size randomness and spatial distribution randomness.

Based on the modeling method of heterogeneous rocks, the SRM numerical model can be built and by adjusting the boundary between soil and rock, an SRM numerical model with any rock content can be obtained. The reliability and robustness of the new modeling method can be verified by uniaxial compression simulation. In addition, this paper investigates the effects of rock topology, rock content, slope height and slope inclination on the stability of SRM slopes.

Investigations of the influences of rock content, slope height and slope inclination of SRM slopes showed that the slope height had little effect on the failure mode. The influences of rock content and slope inclination on the slope failure mode were significant. With increasing rock content and slope dip angle, SRM slopes gradually transitioned from a single shear failure mode to a multi-shear fracture failure mode, and shear fractures showed irregular and bifurcated characteristics in which the cut-off values of rock content and slope inclination were 20% and 80°, respectively.

This paper proposed a new modeling method for SRMs based on FDEM, with rocks having random shapes, sizes and spatial distributions.

Educational accountability and student achievement polarization, which result in high dropout rates, pose significant challenges and pressures on teachers' pedagogical leadership. Whether pedagogical leadership, which originates in the Western educational environment, can significantly improve student achievement in Chinese high schools remains unclear. This concept has not yet been fully explored in the Chinese educational environment, and its direct impact on student achievement and the mediating role of English teaching methods remain to be investigated. However, existing research has concentrated on the effectiveness of principals' pedagogical leadership, with variations in teachers' pedagogical leadership practices. Many reform measures have been implemented in China to improve student achievement, but past educational practices have analyzed the impact on student achievement from a single instructional leadership, school capital or teaching method perspective. Furthermore, there is a lack of multidimensional and systematic assessments of the direct effects of teacher pedagogical leadership on student achievement and the mediating effects of English teaching methods.

To address this gap, this study analyzed the impact of teachers' pedagogical leadership on student achievement and the mediating effect of English teaching methods with the support of the theory of action for teacher leadership, specifically using pedagogical leadership and English teaching methods models.This study conducted a questionnaire survey of 968 participants in Taian City, China, and quantitatively analyzed the data using SmartPLS structural equation modeling (SEM).

This study revealed that pedagogical leadership has a positive direct effect on student achievement. Meanwhile, among the four mediating factors, the Direct Method, Audio-Lingual Method and Communicative Language Teaching had significant mediating effects.

This study shows that the effective use of academic and professional capital allocation in pedagogical leadership, combined with effective measures of using multiple effective English teaching methods, helps achieve high-quality student achievement.

A multi-objective mixed-integer linear program (MILP) model is developed to address this problem. The primary objective is to minimize the total restructuring cost, while the secondary objective aims to enhance the customer service level. To tackle the NP-hard nature of the problem, the non-dominated sorted genetic algorithm (NSGA-II) and a hybrid NSGA-II with the ɛ-constraint method are employed. The hybrid method combines the strengths of the ɛ-constraint method with NSGA-II. Various performance metrics, including the number of Pareto solutions (NPS), normalized set coverage and spacing metrics, are utilized to compare the characteristics of the non-dominated fronts obtained by NSGA-II and the hybrid methods.

The Restructuring Facility Location Problem involves the closure, resizing or opening of a group of facilities and the assignment of customers to these selected facilities. The objective is to provide the required service to customers while minimizing the overall restructuring costs. This paper introduces a novel multi-objective model for hierarchical facilities called the Multi-Objective Restructuring Hierarchical Facility Location Problem (MO-RHFLP). The model specifically includes primary- and secondary-level facilities, with the primary facility offering broad coverage. In MO-RHFLP, customers within the coverage range of the primary facility can receive service from there.

The results demonstrate that the NSGA-II-based method performs well in terms of normalized set coverage and spacing metrics. However, the hybrid method outperforms NSGA-II in these aspects. Additionally, the hybrid method achieves a mutation in the NPS metric.

The present study, from three perspectives, has continued the way of the previous studies in restructuring channels. First, the multi-objective problem of restructuring the bi-level network executed in this study contains both levels of the network opening, closing and resizing. Taking a different perspective, the MO-RHFLP problem is introduced through the formulation of a multi-objective MILP model. This model serves as a framework for addressing the MO-RHFLP. By developing the hybrid ɛ-constraint method with NSGA-II, we solve the proposed problem.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

This study aims to examine the effects of payment methods used in mergers and acquisitions (M&A) conducted by UK companies spanning the period from 2007 to 2019.

The study used the estimated expected returns method to identify abnormal returns during the deal announcement period, applying event study analysis with both univariate and multivariate regression models to detect cumulative abnormal returns around the announcement timeframe.

The results show a short-term positive return increase for acquiring firms, controlling for deal-specific characteristics like target firm location and payment methods. The authors observed a preference for cash financing across domestic and cross-border transactions. Multivariate analysis revealed insignificance between payment methods and deal characteristics like cross-border acquisitions and diversification.

The study’s focus on publicly traded firms in the UK and the absence of a comparative analysis across different regions and markets limits the sample size and may impact the generalizability of findings.

The study proposes three practical implications. Firstly, firms should tailor payment methods to each transaction, aligning with strategic goals to optimize value and mitigate risks. Secondly, decision-makers must prioritize comprehensive due diligence and strategic alignment throughout M&A processes to enhance success and maximize synergies. Finally, analysing broader strategic contexts and regulatory landscapes when structuring transactions enables goal attainment, such as market expansion or value creation.

The study’s findings can promote transparency and accountability among corporate decision-makers in M&A transactions. Stakeholders can advocate for transparent decision-making processes, enhancing trust in corporate governance.

This study provides valuable insights into the impact of payment methods on shareholder value in M&A transactions involving UK companies, informing strategic decision-making and contributing to the understanding of corporate finance dynamics.

Project bundling is an innovative practice that groups or bundles several infrastructure projects into a single contract. While project bundling has various benefits, agencies are facing some challenges when bundling their projects, including properly assessing the feasibility (or infeasibility) of project delivery methods (PDMs) of interest. More specifically, project owners face the challenge of properly selecting between traditional and alternative PDMs for their bundled projects. Although some research efforts were devoted to providing guidelines in relation to different aspects related to project bundling, no previous study was conducted to help project owners performing PDMs-related feasibility analysis for bundled projects, which differ from normal, singly delivered projects. To fill this knowledge gap, this paper develops a decision-support tool that assists agencies in deciding whether they should select a traditional or alternative PDM (i.e. whether to go with the Design-Bid-Build (DBB) PDM or not) for their bundled projects.

An analytical methodology comprised of four main steps was followed in this paper. First, an expert survey was developed and distributed to industry experts to quantify the importance of 25 project bundling objectives. Second, principal component analysis was used to determine the weights for the different project bundling objectives. Third, a series of statistical tests was implemented to identify different feasibility tiers. Fourth, a user-friendly decision-support tool was developed, and its capabilities were demonstrated.

The results showed that six tiers exist to classify the feasibility (or infeasibility) of traditional PDMs (i.e. the DBB method) for bundled projects. The research outcomes have also reflected that the following five project bundling objectives contribute the most to making traditional PDMs (i.e. the DBB method) more feasible for bundled projects: (1) Having well-defined design features; (2) Requiring prior knowledge or experience with similar project size and scope; (3) Completing the overall project on schedule; (4) Keeping rate of expenditures within cash flow plan; and (5) Acquiring specific legislative, regulatory and jurisdictional requirements early on.

This research adds to the body of knowledge by equipping agencies and project owners with a decision-support system that helps them identify whether traditional or alternative PDMs are more appropriate for the specific objectives of their bundling program(s). By making the right PDM decision, project owners can enhance their bundling practices (especially in relation to the PDM proper selection) and ultimately the performance of their bundled projects.