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Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

Herein, the authors report the effects of printing parameters, joining method, and annealing conditions on the structural performance of fusion-joined short-beam sections produced by additive manufacturing.

The authors first identified appropriate printing parameters for joining segmented short beams and then used those parameters to print and fusion-join segments with different configurations of stiffeners to form a longer section of a wing or small wind turbine blade structure.

It was found that the beams with three lateral and three base stiffening ribs give the highest flexural strength among the three beams investigated. Results on joined beams annealed at different conditions showed that annealing at 70 °C for 0.5 h yields higher performance than annealing at the same temperature for longer times. It is also found that in the case of the hot-plate-welded three-dimensional (3D)-printed structures, no annealing is needed for reaching a high strength-to-weight ratio, but annealing is helpful for maximizing the modulus-to-weight ratio. Both thermal buckling and edge wrapping were observed under annealing at 70°C for 0.5 h for 3D-printed beams comprising two lateral and four base stiffening plates.

Fusion-joining of additively manufactured segments is needed owing to the constraint in building volume of a typical commercial 3D-printer. However, study of the effect of process parameters is needed to quantify their effect on mechanical performance. This investigation has therefore identified key printing parameters and annealing conditions for fusion-joining short segments to form larger structures, from multiple 3D-printed sections, such as wind blade structures.

This study aims to empirically investigate the relationship between artificial intelligence (AI) in marketing (AIM) and business performance from the resource-based view (RBV) perspective.

A survey strategy was used in this study to collect data from 225 small and medium enterprises (SMEs) respondents who were on the registered list of the Ghana Enterprise Agency in the Eastern Region of Ghana. Structural equation modeling – path analysis was used to estimate the impact of AIM on the performance of SMEs.

The analyzed data shows that AIM has significant impact on the financial performance, customer performance, internal business process performance and learning and growth performance in the case of SMEs in Ghana. This study establishes the significance of AIM approach in achieving financial performance, customer performance, internal business process performance and learning and growth performance through the application of AIM determinants including, Internet of Things (IoT), collaborative decision-making systems (CDMS), virtual and augmented reality (VAR) and personalization.

Aside the aforementioned significance of this research study, this study has limitations. The sample size of this research study can be expanded to include SME respondents in other geographical areas that were not considered in this study. Future research studies should concentrate on how AIM can analyze customer communications and information such as posts on social media to develop future communications that may enhance customer engagement.

The practical implications comprise of two key items. First, this research study encourages SME owners and managers to develop an AIM method as a fundamental strategic goal in their pursuit to improve SME performance. Second, SME owners and managers should increasingly implement the four determinants of AIM indicated in this research study (i.e., IOT, CDMS, VAR and personalization) to develop essential resources for effective application of AIM to improve their performance.

The results of this study provide a strong support to RBV theory and the proposition that AIM and its determinants (i.e., IOT, CDMS, VAR and personalization) should be recognized as an essential strategic resource for improving the performance (i.e., financial performance, customer performance, internal business process performance and learning and growth performance) of SMEs. This study also contributes to the current body of knowledge on AIM and management, particularly in the context of an emerging economy.

The effectiveness of the Just-in-Time (JIT) production system in non-Japanese contexts is a topic of diverse findings. This study conducts a meta-analysis of empirical research on JIT and its relationship with performance, focusing on studies published since 1995. Additionally, it examines the moderating influence of National Culture (NC) values on JIT outcomes.

A total of 59 empirical studies with 211 effects and 17,008 observations from 18 countries are meta-analyzed. A meta-regression using hierarchical linear modeling (HLM) is performed to explore how four dimensions of National Culture (NC) moderate the impact. (viz. institutional collectivism, uncertainty avoidance future orientation, and power distance,) based on the Global Leadership and Organizational Behavior Effectiveness (GLOBE) culture model.

The meta-analysis results show that improved production efficiency, product quality and reduced wastes achieved through JIT deployments translate into the overall performance of organizations. The meta-regression results shed light on how local cultures influence the effectiveness of JIT across different countries.

The findings of meta-analysis have implications for multinational manufacturers in realizing efficacy of JIT. The research adds to the international operations management literature by examining how NC values influence strategies and decisions in operations management.

The study examines the impact of macroeconomic risk and volatility associated with key macroeconomic indicators on financial market uncertainty; and the extent to which governance and institutional structures moderate such relationships.

The study employs data from 33 countries in Sub-Saharan Africa (SSA) for the period between 1996 and 2019. Variable derivation techniques such as the generalized autoregressive conditional heteroskedasticity (GARCH) for deriving volatility data, and the principal component analysis (PCA) for index construction were employed. The data is examined using the two-step system generalized method of moments (TS-SGMM) technique.

Empirical results suggest that macroeconomic risk and exchange rate volatility heighten financial market uncertainty among economies in the sub-region. Further empirical estimates show that institutional quality and government effectiveness have a negative moderating effect on the nexus between macroeconomic risk, inflation uncertainty, GDP growth, exchange rate, and financial market uncertainty.

The key macroeconomic conditions with the propensity to foment financial market uncertainty are worth monitoring with adequate buffers to mitigate their impacts on the financial market.

Compared to related studies, this study focuses on uncertainty associated with financial markets among emerging economies in sub-Saharan Africa (SSA) instead of the performance of the financial markets or specific financial market indicators such as the stock market; and the extent to which a host of macroeconomic conditions influence such uncertainty. For instance, Abaidoo and Agyapong (2023) focused on the impact of macroeconomic indicators or conditions on the performance of the financial market and the efficiency of financial institutions respectively instead of the uncertainty or risk associated with the financial market as pursued in the current study. This differing approach is pursued with the goal of proffering appropriate strategies for policy makers towards assuaging the financial market risk (uncertainty) due to macroeconomic dynamics. We further examine how the various fundamental relationships may be moderated by effective governance and institutional quality.

The role of construction industry in harnessing human and material resources of a nation cannot be overemphasised; hence, the emergence of the requirement of leadership. This study aims to assess the usage of factors of transactional leadership style (TSLS) by the project managers (PMs) in the Nigeria construction industry.

A survey research design approach with questionnaire as an instrument of gathering data was adopted. Out of 1,233 questionnaires distributed, data from 975 received with acceptable feedback were analysed using both descriptive and inferential statistics.

This study revealed the following factors under active management by exception as the TSLS factors often employed by PMs in Nigeria. These factors are the three frequently used factors: they are: “always give clear and final instructions to be implemented in the project”; “always observed the progress of the project, assessed risk and took precaution to avoid mistakes in the project”; and “always closely monitor performance for errors needing correction”. This study concludes that it is imperative for PMs in the study area to adopt and incorporate these factors to ensure continuous successful delivery of construction projects.

Therefore, it is hoped that the findings of this research will help the construction industry managers to address the age-long but unrecognised leadership problem confronting the sector, thereby boosting project delivery.

The findings establish the appropriateness or otherwise of adoption of factors of transactional leadership, either in part or a whole.

The core of this study is women in mining. The aim of this study was to analyze the perception of women, about their work environment, their career, the human resources policies and practices and the work–family balance in the context of a multinational organization in the sector.

The case study was carried through semi-structured interviews with 27 women who work in this organization.

Although women report that they are capable of exercising any position/function in the company, they perceive wage gap in the investigated organization; that maternity leave and the fact of having children impact their career and their rise to higher positions in the organizational hierarchy negatively; situations of prejudice and discrimination experienced at work. Sometimes subtly, sometimes not so subtly, but they still constitute barriers faced by women in mining.

Although the debate around the issue of gender inequality in organizations is not recent, little has been produced about the working condition of women in occupational fields where male domination is strong, such as mining.

The foreign direct investment (FDI) motivations of emerging market multinational enterprises (EMNEs) are mainly twofold: acquisition of strategic assets in foreign markets, and foreign market penetration. While prior studies have delivered valuable insights, findings regarding the performance of those two types of FDI remain somewhat inconsistent or inconclusive. This study aims to develop complementary perspectives that can motivate scholars to explore the internal mechanisms of achieving goals for these two FDI types by providing a review of prior literature on EMNEs’ knowledge- and market-seeking FDI.

Indexed to the EBSCO database and Google Scholar from 2000 to 2020, 73 articles from 13 journals were selected and reviewed to identify the main research future research agendas.

Our findings show that the purpose of EMNEs’ FDI can be divided into value creation and value capturing, with the former pursuing knowledge-seeking and the latter pursuing market-seeking, according to our study, which draws on insights from innovation-focused literature.

International business (IB) scholars have extensively studied both knowledge-seeking and market-seeking outward FDI of EMNEs for decades. Our study contributes to the literature by providing the potential for integrating IB and innovation studies to extend the scope of EMNEs studies.

The current scenario shows increased adoption of off-chain voting applications in decentralized autonomous organizations (DAOs) to avoid complexity and costs for user engagement in their governance. This paper aims to comprehend the trends and patterns within the DAO community with off-chain voting systems and blockchain usage.

This exploratory study uses a two-pronged quantitative approach, combining descriptive statistical analysis and fuzzy-set qualitative comparative analysis. Input data from different DAO proposals and votes were analyzed.

Results indicate that the 179 DAOs analyzed account for 26,538 proposals and 1,268,474 followers of the entire Snapshot base in January 2023, showing high concentration in engagement. The preferred social media communication channels are Twitter and GitHub, showing a decrease of Ethereum as a main chain, with Polygon and Binance Smart Chain already accounting for 22%. Basic voting can lead to greater user involvement in the voting process.

This study complements the traditional governance theory perspective and advances in the emerging literature on DAOs and blockchain with an empirical analysis.

As blockchain technology gains increasing adoption, comprehending trends and patterns within the DAO community becomes pivotal in identifying opportunities and challenges for innovation and improvement.

Findings can facilitate the development of improved governance models and decision-making processes for DAOs, leading to a more robust and sustainable ecosystem.

This study presents a broader IT-based governance foresight via blockchain while providing an understanding of the voting process, technology features and governance mechanisms for social community engagement and decision-making in DAOs.

In this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI).

The proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control.

The steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller.

The hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions.