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As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

Rapid changes in technologies and customer preferences are increasing market uncertainty. Hence, despite the benefits of reactive and adaptive marketing in the industrial market, such marketing is sometimes insufficient for suppliers to survive and succeed. This phenomenon is prevalent among the small and medium-sized suppliers (SMSs) who fail to build technological capability. This suggests that SMSs should be entrepreneurial to survive and succeed in today’s environment. Against this backdrop, the purposes of this study are to understand the process by which entrepreneurial marketing of SMSs enhances their technological capability and to explore factors that stimulate SMSs to implement entrepreneurial marketing.

Based on a survey data set on 249 industrial SMSs in South Korea, the authors test the hypotheses formulated in this work using structural equation modeling.

Based on effectuation theory, this study conceptualizes entrepreneurial marketing as a process by contrasting entrepreneurial marketing with reactive and adaptive marketing. The results show that proactive market orientation and subsequent exploratory market behavior enhance technological capability, and proactive market orientation is stimulated by relational satisfaction and perceived technological turbulence.

This study advances effectuation theory in the industrial marketing literature by exploring the value of entrepreneurial marketing in today’s industrial market. Further, this study extends the entrepreneurial marketing literature by conceptualizing and testing the process by which entrepreneurial marketing of SMSs enhances their technological capability.

This work aims to develop a web platform for inspecting roof structures for technical assistance supported by drones and artificial intelligence. The tools used were HTML, CSS and JavaScript languages; Firebase software for infrastructure; and Custom Vision for image processing.

This study adopted the design science research approach, and the main stages for the development of the web platform include (1) creation and validation of the roof inspection checklist, (2) validation of the use of Custom Vision as an image recognition tool, and (3) development of the web platform.

The results of automatic recognition showed a percentage of 77.08% accuracy in identifying pathologies in roof images obtained by drones for technical assistance.

This study contributed to developing a drone-integrated roof platform for visual data collection and artificial intelligence for automatic recognition of pathologies, enabling greater efficiency and agility in the collection, processing and analysis of results to guarantee the durability of the building.

Recently, the popularity of software has grown significantly in the market. Enhancement of software is needed to decrease the burden of getting high-quality and reliable software. To achieve this, the software is upgraded by adding new features to the previous version. Therefore, adding new features in the last version to be consistent with the earlier version is challenging. This paper aims to discuss the optimal software enhancement and customer growth model.

This paper discusses a model when new features are added to the software, and the customers' adoption of the software is presented as a customer growth model. An optimal control problem is introduced to maximize the profit obtained from the software system over the system's lifetime period. Total gain is calculated by the value generated from selling the software over the total expenditure during the software development process. The closed-form solution and some theoretical results are presented using the optimal control-theoretic approach. The theoretical results are supported by a numerical example.

This paper gives several substantive insights during sensitivity analysis and provides essential results. The results discussed here are compatible with the actual scenario and useful in software enhancement.

The authors have proposed a new feature growth and customer growth model to maximize the total profit using optimal control theory.

The current work sought to investigate the mediating effect of supply chain ambidexterity on the relationship between Industry 4.0 capabilities and operational performance of manufacturing firms in Jordan.

Data collection was carried out through a survey with 253 respondents from manufacturing firms in Jordan through the first quarter in 2023. The quantitative approach and structural equation modeling (SEM) were applied to analyze the collected data. Dynamic capabilities view (DCV) theory was the adopted theoretical lens for this work.

The results demonstrated that Industry 4.0 capabilities positively and significantly affect exploration, exploitation and operational performance. In addition, the results confirmed that exploration and exploitation positively and significantly affect operational performance. Further, it is also found that exploration and exploitation in the supply chain positively and significantly mediate the relationship between Industry 4.0 capabilities and operational performance.

This study focuses on this gap to deepen the understanding of operational performance in a recent manufacturing environment under various factors and perspectives (Industry 4.0 capabilities and supply chain ambidexterity).

Researchers continue to address the concept of self-disclosure because it is foundational for helping social networking sites (SNS) function and thrive. Nevertheless, the authors' literature review indicates that uncertainty remains around the underlying mechanisms and factors involved in the self-disclosure process. The purpose of this research is to better understand the self-disclosure process from the lens of dual-process theory (DPT). The authors consider both the controlled factors (i.e. self-presentation and reciprocity) and an automatic factor (i.e. social influence to use an SNS) involved in self-disclosure and broaden The authors proposed a model to include the interactive facets of enjoyment.

The proposed model was empirically validated by conducting a survey among users of WeChat Moments in China.

As hypothesized, this research confirms that enjoyment and automatic processing (i.e. social influence to use an SNS) are complementary in the SNS self-disclosure process and enjoyment negatively moderates the positive relationship between controlled factor (i.e. self-presentation) and self-disclosure.

Theoretically, this study offers a new perspective on explaining SNS self-disclosure by adopting DPT. Specifically, this study contributes to the extant SNS research by applying DPT to examine how the controlled factors and the automatic factor shape self-disclosure processes and how enjoyment influences vary across these processes – enriching knowledge about SNS self-disclosure behaviors. Practically, the authors provide important design guidelines to practitioners concerning devising mechanisms to foster more automatic-enjoyable value-added functions to improve SNS users' participation and engagement.

This study aims to empirically examine the influence of Islamic corporate social responsibility (ICSR) on social welfare moderated by financial fraud.

The method used was the mix method. The number of respondents was 410. They combined the moderate regression analysis with PROCESS Andrew F Hayes to test the research hypothesis. After conducting the survey, it was continued by conducting interviews with the village community and the head of the village.

The first finding of this study is that ICSR has a significant positive effect on social welfare. The second finding is that financial fraud weakens the influence of ICSR on social welfare. The results of the interviews also confirmed the two findings of this study.

The high level of bias in answering the questions is due to the low public knowledge of ICSR. In addition, the interviews still needed to involve the oil and gas companies and government.

The main implication is improving social welfare, especially for those affected by offshore oil drilling. Furthermore, stakeholders are more sensitive to the adverse effects of financial fraud. Finally, to make drilling companies more transparent and on target in implementing ICSR.

The main novelty in this research is using of the mixed method. In addition, applying financial fraud as a moderating variable is rarely studied empirically.

This study aims to use emotions-as-social information theory to investigate how physical (customer perceived store atmosphere) and social servicescapes (customer information searching) influence the effects of employees’ positive affective displays on customer outcomes via customer positive moods and positive expectation disconfirmation.

This research included two studies, each using a distinct research design, to empirically test the proposed model. Study 1 involved 200 observational data points on objective purchase amounts from designer watch shops. In Study 2, data were collected from 230 customers in designer jewelry stores.

The results of path analyses revealed that: employee positive affective displays are positively associated with customer purchase outcomes; employee positive affective displays had positive indirect effects on customer purchase outcomes by enhancing customer positive moods and positive expectation disconfirmation; these positive indirect effects were strengthened when customers engaged in information search behaviors; and these positive indirect effects were attenuated when customers perceive store atmosphere as favorable, indicating a substitution effect of customer perceived store atmosphere.

Previous research has not thoroughly examined the role of the servicescape in moderating the effects of employees’ positive affective displays on customer purchase outcomes. This present study not only clarified the affective and cognitive mechanisms that link employees’ positive affective displays on purchase outcomes but also identified servicescape as a critical boundary condition of these effects.

The purpose of this study is to use the corrected stress field theory to derive the shear capacity of geopolymer concrete beams (GPC) and consider the shear-span ratio as a major factor affecting the shear capacity. This research aims to provide guidance for studying the shear capacity of GPC and to observe how the failure modes of beams change with the variation of the shear-span ratio, thereby discovering underlying patterns.

Three test beams with shear span ratios of 1.5, 2.0 and 2.5 are investigated in this paper. For GPC beams with shear-span ratios of 1.5, 2.0 and 2.5, ultimate capacities are 337kN, 235kN and 195kN, respectively. Transitioning from 1.5 to 2.0 results in a 30% decrease in capacity, a reduction of 102kN. Moving from 2.0 to 2.5 sees a 17% decrease, with a loss of 40KN in capacity. A shear capacity formula, derived from modified compression field theory and considering concrete shear strength, stirrups and aggregate interlocking force, was validated through finite element modeling. Additionally, models with shear ratios of 1 and 3 were created to observe crack propagation patterns.

For GPC beams with shear-span ratios of 1.5, 2.0 and 2.5, ultimate capacities of 337KN, 235KN and 195KN are achieved, respectively. A reduction in capacity of 102KN occurs when transitioning from 1.5 to 2.0 and a decrease of 40KN is observed when moving from 2.0 to 2.5. The average test-to-theory ratio, at 1.015 with a variance of 0.001, demonstrates strong agreement. ABAQUS models beams with ratios ranging from 1.0 to 3.0, revealing crack trends indicative of reduced crack angles with higher ratios. The failure mode observed in the models aligns with experimental results.

This article provides a reference for the shear bearing capacity formula of geopolymer reinforced concrete (GRC) beams, addressing the limited research in this area. Additionally, an exponential model incorporating the shear-span ratio as a variable was employed to calculate the shear capacity, based on previous studies. Moreover, the analysis of shear capacity results integrated literature from prior research. By fitting previous experimental data to the proposed formula, the accuracy of this study's derived formula was further validated, with theoretical values aligning well with experimental results. Additionally, guidance is offered for utilizing ABAQUS in simulating the failure process of GRC beams.