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Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

This study aims to develop a self-reflection scale useful for teachers to improve their skills and to clarify the Japanese teachers’ characteristics during mathematics lesson observation (MLO). In MLO, it is important to understand the lesson plan in advance to clarify observation points, and we aim to develop a scale including these points.

Based on the pre-questionnaire survey, nine perspectives and two situations for MLO were extracted. From these, a questionnaire for MLO was created. The results obtained from 161 teachers were examined, and exploratory factor analysis was conducted. ANOVA was conducted to analyze the effect of differences across the duration of teaching experience on the identified factors.

We developed a self-reflection scale consisting of 14 items with three factors: [B1] focus on instructional techniques and evaluation, [B2] focus on proactive problem-solving lesson development and [B3] focus on the mathematical background of the learning content. While duration of teaching experience showed no effect, three factors of the self-reflection scale for MLO showed a significant effect. Further multiple comparisons revealed the degree of focus was [B2]>[B1]>[B3].

Teachers who use this developed scale may grasp the strengths and weaknesses of their own MLO, which leads to self-improvement. The perspectives emphasized in lesson observation are the same when creating lesson plans and implementing lessons, leading to lesson improvement. Furthermore, based on the characteristics of teachers revealed, new training programs regarding MLO can lead to higher-quality lesson studies.

This paper aims to analyze the economic-financial performance (EFP) and value creation (VC) in the Brazilian construction industry.

Based on the theories of strategy and finance, a quantitative-qualitative, descriptive and explanatory and applied study was carried out, contrasting the performance of the Direcional company and the civil construction industry – both listed on the Brazilian Stock Exchange and the Over-the-Counter Market (B3)

The analysis of the EFP in the Brazilian construction industry shows that EZTEC, Helbor, Trisul and Direcional were the companies with the best EFP in the period. The analysis of the Economic Value Added (EVA®, henceforth EVA), as a VC metric and basis for assessing the relative technical efficiency score by Data Envelopment Analysis (DEA®, henceforth DEA), revealed that the companies Direcional, EZTEC, MRV and CR2 were considered efficient throughout the period covered. The multicriteria methodology for empirical testing of the EFP and VC allowed not only contrasts Direcional's results with the other companies of the construction industry but also offered a complementary tool for comparative analysis of enterprises of different sizes, structures and realities.

Regardless of any contextual limitations, from a theoretical point of view, the research not only helps fill the research gap aforementioned but also expands knowledge on the topic and demonstrates how this multi-criteria methodology (integrating DEA and EVA) can be used to assess EFP and VC in addition to traditional tools. However, this new approach evaluates, at the same time, corporate and sectorial effectiveness by contrasting the efficiency and efficacy (simultaneously) in the generation of performance and value of a company in relation to the industry.

Significant implications for managerial practice could be noted by offering a tool to improve company performance and creating a competitive benchmarking process for analysts, investors, managers, financing agencies, shareholders, policymakers and business owners, as well as organizations and sectors in similar situations – who need to assess the EFP and VC holistically and improve their decision-making processes.

The uniqueness and innovation of this research come from the original multi-criteria methodology developed, applied and validated for analysis of EFP and VC. This methodology was operationalized through DEA applied to the companies' EVA, making it possible to compare corporate results and those of the whole industry in a balanced way – an unexplored issue in the literature, especially in emerging economies, opening several avenues for future research.

Este artículo tiene como objetivo analizar el desempeño económico-financiero (DEF) y la creación de valor (CV) en la industria de la construcción brasileña.

Con base en las teorías de estrategia y finanzas, se realizó un estudio cuanti-cualitativo, descriptivo, explicativo y aplicado, contrastando el desempeño de la empresa Direcional y de la industria de la construcción civil, ambas cotizadas en la Bolsa y Mercado Extrabursátil Brasileña (B3).

El análisis de la DEF en la industria de la construcción brasileña muestra que EZTEC, Helbor, Trisul y la Direcional fueron las empresas con el mejor desempeño en el período. El análisis del Valor Económico Agregado (en adelante EVA), como métrica de CV y base para evaluar el puntaje de eficiencia técnica relativa mediante Análisis Envolvente de Datos (en adelante DEA), reveló que las empresas Direcional, EZTEC, MRV y la CR2 se consideraron eficientes durante todo el período cubierto. La metodología multicriterio para pruebas empíricas de la DEF y CV permitió no sólo contrastar los resultados de la Direcional con los de otras empresas del sector de la construcción, sino que también ofreció una herramienta complementaria para el análisis comparativo de empresas de diferentes tamaños, estructuras y realidades.

La singularidad y la innovación de esta investigación provienen de la metodología original multicriterio desarrollada, aplicada y validada para el análisis de DEF y CV. Esta metodología fue operacionalizada a través de DEA aplicado al EVA de las empresas, permitiendo comparar los resultados corporativos y los de toda la industria de manera equilibrada – un tema inexplorado en la literatura, especialmente en las economías emergentes, abriendo varias vías para futuras investigaciones.

Independientemente de las limitaciones contextuales, desde un punto de vista teórico, la investigación no solo ayuda a llenar el vacío mencionado anteriormente, sino que también amplía el conocimiento sobre el tema y demuestra cómo esta metodología multicriterio (integrando DEA y EVA) puede utilizarse para evaluar el DEF y CV además de las herramientas tradicionales. Sin embargo, este nuevo enfoque evalúa, al mismo tiempo, la efectividad corporativa y sectorial contrastando la eficiencia y eficacia (simultáneamente) en la generación de desempeño y valor de una empresa en relación con la industria.

Se podrían observar implicaciones significativas para la práctica gerencial al ofrecer una herramienta para mejorar el desempeño de la empresa y crear un proceso de evaluación comparativa competitivo para analistas, inversionistas, gerentes, agencias financieras, accionistas, formuladores de políticas y propietarios de negocios, así como organizaciones y sectores en situaciones similares, que necesitan evaluar el DEF y el CV de manera integral y mejorar sus procesos de toma de decisiones.

This paper aims to investigate how purchasing organizations implement supplier diversity (SD) initiatives over time.

A multiple case study approach was conducted. Data were collected through in-depth interviews with participants from purchasing organizations, intermediary organizations and diverse suppliers.

The research suggests that the SD journey encompasses three different, but interrelated stages before full implementation is achieved: structuring, operation and adaptation. The findings also provide evidence that SD implementation in Brazil is highly influenced by the lack of a consistent knowledge base and the lack of legitimized intermediary organizations.

Using a temporal approach to understand how different practices suggested by the literature have been managed by practitioners over time, this study contributes to the understanding of the path to effective SD implementation and how intra- and interorganizational context influences this journey.

By identifying which practices should be adopted during different phases of SD implementation and proposing ways to overcome some of the inherent challenges, managers can better plan and allocate resources for the adoption of a successful SD initiative.

This research demonstrates how organizations can promote diversity and reduce social and economic inequalities by buying from diverse suppliers.

Using a temporal approach, the research empirically investigates how different purchasing organizations have implemented and managed the known practices and dealt with the challenges faced when trying to adopt SD.

Packaging design has the power to convey persuasive and functional benefits, thereby inducing the brand preference as per empirical studies, but not much has been studied in detail about aesthetic aspects of packaging. This study is a careful attempt to understanding the influence of aesthetic elements—colour, illustrations/images/pictures and information representation’s influence on consumer brand preference for ready-to-eat (RTE) product category of pav bhaji.

The data for this study have been collected in two phases; phase 1 was a quantitative research where structure Google form was used while in order to elite consumer responses regarding their preferences structured interview was conducted in phase 2. Twenty-five respondents were selected using snowball sampling, who were approached for understanding how aesthetic element influenced their brand preference.

This research could help in identifying various underlying subthemes within the three major themes of aesthetic element in packaging. By utilising these, packaging modifications could be made to increase brand preference.

In case of RTE segment, in-store decisions are mostly take in spurn of minutes, hence making it one of essential topics of research. The findings of this research can contribute in placement of correct element in appropriate position to grab consumers’ interest leading to brand preference.

In recent times, reverse logistics (RL) is gaining significant traction in various automobile industries to recapture returned vehicles’ value. A good RL program can lower manufacturing costs, establish a green supply chain, enhance customer satisfaction and provide a competitive advantage. However, reducing disruptions and increasing operational efficiency in the automobile RL requires implementing innovative technology to improve information flow and security. Thus, this manuscript aims to examine the hurdles in automobile RL activities and how they can be effectively tackled by blockchain technology (BCT). Merging BCT and RL provides the entire automobile industry a chance to generate value for its consumers through effective vehicle return policies, manufacturing cost reduction, maintenance records tracking, administration of vehicle information and a clear payment record of insurance contracts.

This research is presented in three stages to accomplish the task. First, previous literature and experts' opinions are examined to highlight certain factors that are an aggravation to BCT implementation. Next, this study proposed an interval-valued intuitionistic fuzzy set (IVIFS) – decision-making trial and evaluation laboratory (DEMATEL) with Choquet integral framework for computing and analyzing the comparative results of factor interrelationships. Finally, the causal outline diagrams are plotted to determine the influence of factors on one another for BCT implementation in automobile RL.

This study has categorized the barriers to BCT implementation into five major factors – operational and strategical, technical, knowledge and behavioral, financial and infrastructural, and government rules and regulations. The results revealed that disreputable technology, low-bearing capacity of IT systems and operational inefficiency are the most significant factors to be dealt with by automobile industry professionals for finer and enhanced RL processes utilizing BCT. The most noticeable advantage of BCT is its enormous amount of data, permitting automobile RL to develop client experience through real-time data insights.

This study reveals several factors that are hindering the implementation of BCT in RL activities of the automobile industry. The results can assist experts and policymakers improve their existing decision-making systems while making an effort to implement BCT into the automobile industry's RL activities.

Although there are several studies on the benefits of BCT in RL and the adoption of BCT in the automobile industry, individually, none have explicated the use of BCT in automobile RL. This is also the first kind of study that has used IVIFS-DEMATEL with the Choquet integral framework for computing and analyzing the comparative results of factor interrelationships hindering BCT implementation in automobile RL activities.

The apparel and textile sector poses a significant environmental challenge due to its substantial contribution to pollution in the form of air, water and soil pollution. To combat these issues, the adoption of sustainable practices is essential. This study aims to identify and analyse the barriers that hinder the progress of sustainability in the apparel and textile industry. By consulting experts in the field, critical barriers were identified and given special attention.

To achieve the research objective, an integrated approach involving Interpretive Structural Modelling (ISM) and fuzzy MICMAC decision-making techniques was employed. The results were further validated through the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method.

The findings highlight that barrier related to clothing disposal, inadequate adaptation to modern technology, challenges affecting sector efficiency and issues related to fashion design are crucial in influencing the remaining six barriers. Based on the outcomes of the DEMATEL method, a comprehensive cause-and-effect diagram was constructed to gain a deeper understanding of these challenges.

This research provides valuable insights for policymakers and stakeholders in the apparel and textile industry. It offers a strategic framework to address and overcome sustainability barriers, promoting the development of a more environmentally responsible and resilient sector.

The purpose of this research is to conduct an in-depth investigation of the barriers apparel and textile sectors. It is feasible that both the management team and the medical experts who provide direct patient care could benefit from this research.

Seaports are vital in facilitating sustainable development, and environmental, social and governance (ESG) factors significantly impact an organization’s performance. Therefore, this study aims to identify and evaluate barriers and strategies of green investments to promote ESG practices within the seaport sector.

To fulfill this aim, a systematic literature review, interpretive structural modeling and the matrix of cross-impact multiplications were applied to classification analysis.

12 barriers were prioritized and categorized by experts in a focus group to optimize efforts and define the materiality of these barriers in implementing ESG strategies within seaport companies.

The implications of this study provide an alternative approach for ESG management in the context of seaports that can be applied in different regions by experts' opinion assessment.

No prior studies assessed the barriers and strategies for green investments in ESG from the port sector perspective.

This article aims at - exploring and prioritizing the barriers to adoption of digitization in supply chains (SCs), categorizing them into sustainability triple bottom line (STBL) based upon their direct impact and suggesting digital technologies to address each barrier.

A five-phase methodology is used which consists of an exploration of 44 barriers to the adoption of digitization in SCs, analysis of 44 barriers for mean, standard deviation and Cronbach alpha based on questionnaire-based feedback of 25 experts, extraction of 10 most significant barriers through 05 experts, followed by categorization of the barriers into STBL referring to their direct impact on STBL, prioritization of ten barriers using Fuzzy Technique for Order Performance by Similarity to Ideal Solution and recommendation of digital technologies to address each barrier.

While all the barriers considered in this study significantly impede the adoption of digitization in SCs, lack of top management commitment (B1) is found to be most crucial while lack of culture toward use of information and communication technology required for digitization (B3) has minimum impact. Large investment in digital infrastructure (B6), difficulty in integration of cyber physical systems (CPSs) on varied platforms (B8) and lack of experts having knowledge of digital technologies (B2) are equally important barriers requiring more attention while adopting digitization in SCs.

This study is mainly based on feedback from 25 seasoned experts; a wider cross section of experts will give more insight.

The outcomes are very significant for organizations looking to adopt digitization in their SCs. Simultaneous consideration to all the barriers becomes impractical hence prioritization of same will be useful for the SC managers to benchmark their preparedness and decide strategies for the adoption of digitization with due consideration toward the impact of barriers on STBL. The digital technologies recommended will further aid in planning the digital strategies to address each barrier.

A unique approach to explore, analyze, prioritize and categorize the barriers to adoption of digitization in SCs is used to provide a deeper understanding of factors deterring the same. It implies that a supportive top management along with systematic allocation of finances plays a crucial role. The importance of availability of digital experts for integrating CPSs on a single platform is also highlighted. The digital technologies recommended will further assist the organizations toward adoption of digitization in SCs with due consideration to STBL.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.