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Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

Gas transmission pipelines are at constant risk of gas leakage or fire due to various atmospheric environments, corrosion on pipe metal surfaces and other external factors. This study aims to reduce the human and financial risks associated with gas transmission by regularly monitoring pipeline performance, controlling situations and preventing disasters.

Facility managers can monitor the status of gas transmission lines in real-time by integrating sensor information into a building information modeling (BIM) 3D model. Using the Monitoring Panel plugin, coded in C# programming language and operated through Navisworks software, the model provides up-to-date information on pipeline safety and performance.

By collecting project information on the BIM and installing critical sensors, this approach allows facility manager to observe the real-time safety status of gas pipelines. If any risks of gas leakage or accidents are identified by the sensors, the BIM model quickly shows the location of the incident, enabling facility managers to make the best decisions to reduce financial and life risks. This intelligent gas transmission pipeline approach changes traditional risk management and inspection methods, minimizing the risk of explosion and gas leakage in the environment.

This research distinguishes itself from related work by integrating sensor data into a BIM model for real-time monitoring and providing facility managers with up-to-date safety information. By leveraging intelligent gas transmission pipelines, the system enables quick identification and location of potential hazards, reducing financial and human risks associated with gas transmission.

Multinational business deliver value via multiple sites with similar operational capacities. The age of the Fourth Industrial Revolution (4IR) delivers significant opportunities for the deployment of digital tools for business optimization. Therefore, this study aims to study the Industry 4.0 implementation for multinationals.

The key objective of this research is multi-site systems integration using a reproducible, modular and standardized “Cyber Physical System (CPS) as-a-Service”.

A best practice reference architecture is adopted to guide the design and delivery of a pioneering CPS multi-site deployment. The CPS deployed is a cloud-based platform adopted to enable all manufacturing areas within a multinational energy and petrochemical company. A methodology is developed to quantify the system environmental and sustainability benefits focusing on reduced carbon dioxide (CO₂) emissions and energy consumption. These results demonstrate the benefits of standardization, replication and digital enablement for multinational businesses.

The research illustrates the ability to design a single system, reproducible for multiple sites. This research also illustrates the beneficial impact of system reuse due to reduced environmental impact from lower CO₂ emissions and energy consumption. The paper assists organizations in deploying complex systems while addressing multinational systems implementation constraints and standardization.

The application of three-dimensional (3D) printing technology in construction projects is of increasing interest to researchers and construction practitioners. Although the application of 3D printing technology at various stages of the project lifecycle has been explored, few studies have identified the relative importance of critical success factors (CSFs) for implementing 3D printing technology in construction projects. To address this research gap, this study aims to explore the academics (i.e. researchers) and construction practitioners’ perspectives on CSFs for implementing 3D printing technology in construction projects.

To do this, a questionnaire was administered to participants (i.e. academics and construction practitioners) with knowledge and expertise in 3D printing technology in construction projects. The collected data were analysed using mean score ranking, normalization and rank agreement analysis to identify CSFs and determine the consistency of the ranking of CSFs between academics and construction practitioners. In addition, exploratory factor analysis was used to identify the relationships and underlying constructs of the measured CSFs.

Through a rank agreement analysis of the collected data, 11 CSFs for implementing 3D printing technology were retrieved (i.e. 17% agreement), indicating a diverse agreement in the ranking of the CSFs between academics and construction practitioners. In addition, the results show three key components of CSFs including “production demand enabling CSFs”, “optimize the construction process enabling CSFs” and “optimized design enabling CSFs”.

This study highlights the feasibility of implementing the identified CSFs for 3D printing technology in construction projects, which not only serves as a reference for other researchers but also increases construction practitioners’ awareness of the practical benefits of implementing 3D printing technology in construction projects. Specifically, it would optimize the construction lifecycle processes, enhance digital transformation and promote sustainable construction projects.

The increasing adoption of informatization in the architecture, engineering, and construction (AEC) industries has raised the competency requirements for AEC practitioners. However, existing research primarily focuses on the integration of emerging technologies in AEC education programs, with little attention to the development of informatization-related competencies. Therefore, this paper aims to explore the competency requirements in the information age of the AEC industry.

Taking a policy perspective, this study investigates the competency requirements within the context of AEC industry informatization. By employing a competency-based theoretical framework, content analysis is conducted on China's policy document, the Outline of the Development of Informatization in the Construction Industry.

The study identifies crucial emerging technologies in the AEC industry, such as building information modeling (BIM), Big Data, Internet of things, networking, and cloud computing, along with their application scenarios. It considers various market players, including survey and design institutes, construction companies, and general contracting enterprises. Comparative analysis reveals the technology application patterns of these market players, shedding light on their preferences and perspectives. Based on these findings, the study proposes recommendations for competency requirements in the AEC industry.

This study extends the competency-based theory to AEC education from a macro perspective. The findings enhance understanding of informatization by providing insights into the related technologies, their applications, and the market players utilizing them. Moreover, the study's results have significant implications for AEC education, particularly in the design of curriculum systems for emerging technology-related fields.

Listening is often considered the cornerstone of the communication process, with feedback being a crucial skill for effective management. The primary objective of this article was to investigate the relationship between managers’ listening skills and feedback skills from their subordinates’ perspectives. Moreover, it explores the mediating effect of message-sending skills and the ability to deal with interference in this relationship.

This article deployed a quantitative, descriptive research design. The authors developed and distributed a self-administered questionnaire via non-probability convenience sampling, resulting in 304 useable responses.

The results of the main direct effect test (model 1) indicate that listening is positively associated with feedback. Model 2 established that message-sending skills did not directly mediate that relationship. On the other hand, the ability to deal with interference was found to mediate the relationship. Finally, model 4 showed the multi-mediating effect of message-sending skills and the ability to deal with interference in the relationship between listening and feedback.

As far as the researchers are aware, this paper is the first of its kind to show the ability to deal with interference as a mediating factor in a statistical model. Moreover, this study is the first to present a continuous intermediary role played by message-sending skills and the ability to deal with interference in the relationship between listening and feedback.

Automation and the new buzzword, “Industry 4.0”, have dominated the media headlines in recent months. In this scenario, apparel manufacturers should not only install automatic machines but also standardise them based on specific industry requirements, and precise measures are required for daily target demands.

This study demonstrates the application of Predetermined Motion and Time System (PMTS) tools in various automatic and semiautomatic machines to obtain higher productivity and the highest utilisation percentage of operator and automats between the 1:1 and 1:2 man vs machine configuration models. In this study, timeSSD® was used to calculate the micro motions of humans. In addition, a video annotation and modelling software Tracker was used to calculate high-speed machine movements with loading frames of 30 FPS.

After the implementation of PMTS tools, it was found that for a 1:1 man vs machine configuration, the operator utilisation is 75% per shift and the operator idle time is 50% per cycle time, and the operator is sitting idle for 2 h per 8 h of shift. So, there is scope to improve the utilisation and idle time of operator.

With the PMTS software, an industrial engineer professional with knowledge of the micromotion economy can only calculate micromotion.

Exploring the first time in the world to establish standard allowed minute (SAM) of a partly automated single-unit sewing machine with partial human intervention and a semiautomatic machine. Theoretical underpinnings indicate that manufacturers use the experience to determine the SAM of any operation over time, necessitating this work to calculate standard minutes automatically.

The purpose of this paper is to develop a building information modelling (BIM)-based multi-objective optimization (MOO) framework for volumetric analysis of buildings during early design stages. The objective is to optimize volumetric spaces (3D) instead of 2D spaces to enhance space utilization, thermal comfort, constructability and rental value of buildings

The integration of two fundamental concepts – BIM and MOO, forms the basis of proposed framework. In the early design phases of a project, BIM is used to generate precise building volume data. The non-sorting genetic algorithm-II, a MOO algorithm, is then used to optimize extracted volume data from 3D BIM models, considering four objectives: space utilization, thermal comfort, rental value and construction cost. The framework is implemented in context of a school of architecture building project.

The findings of case study demonstrate significant improvements resulting from MOO of building volumes. Space utilization increased by 30%, while thermal comfort improved by 20%, and construction costs were reduced by 10%. Furthermore, rental value of the case study building increased by 33%.

The proposed framework offers practical implications by enabling project teams to generate optimal building floor layouts during early design stages, thereby avoiding late costly changes during construction phase of project.

The integration of BIM and MOO in this study provides a unique approach to optimize building volumes considering multiple factors during early design stages of a project

The relationship between the various existing smell taxonomies and the smell impacting factors has been established. The ideology is to identify the most critical smell influencing factors in the vicinity of various software development environments.

To fulfill the said task, the utilization of the amalgamation of two multicriteria decision-making techniques, namely, Entropy method and CODAS method, is presented.

Through this article, the most critical smell impacting criteria with respect to the smell taxonomies is identified. Furthermore, the behaviour of 4 software development principles was then analysed, and their working state has been successfully assessed.

The ideology to study design-related smells in the software system has been studied by a lot of researchers. Some of them have worked upon their detection and the corresponding refactoration process with the help of several algorithms like machine learning and artificial intelligence. But how and to what extent these design-related smells impact the software development environment has remained out of the limelight till now. Through this article, this research gap has been identified, and an attempt to fill it has been made.

This study explores how strategic human resource practices enhance the competitive capability of differentiation and cost-effectiveness by leveraging knowledge resources in Indian IT/software organizations. It examines the mediating effect of knowledge management (KM) processes in the relationship between strategic HR practices, competitive differentiation and cost-effectiveness capabilities.

An online questionnaire survey collected data from 380 knowledge workers in 25 IT/software and consultancy firms. The authors checked data reliability and validity by conducting exploratory factor analysis in SPSS and confirmatory factor analysis in AMOS. The authors evaluated hypotheses using path analysis in structural equational modeling in AMOS.

Strategic HR practices significantly and positively affect KM processes and competitive capabilities-differentiation and cost-efficiency. Both strategic HR practices and KM processes have a closer association with differentiation than cost-effectiveness. Knowledge management processes significantly and positively mediate between strategic HR practices and competitive capabilities. The mediation is more substantial in predicting differentiation than cost-effectiveness.

It is a cross-sectional study with a constrained capacity to predict accurate causal inferences; The authors call for future studies with longitudinal design and objective measures. Further studies are required to explore the impact of various strategic HR configurations on KMP to understand how different routes stimulate a particular competitive strategy. This conceptual framework can be validated across different industry types and sizes.

This study provides practical insights to HR and knowledge managers regarding devising HR and KM processes to accomplish the goals of differentiation and cost-effective, competitive strategies. This study highlights that leveraging human capital for effective KM is crucial for gaining a competitive advantage.

The paper adds to the strategic HR and KM literature by exploring the mediating role of KM processes in enabling strategic HR processes to enhance differentiation and cost-effective, competitive strategies. It provides original empirical evidence from knowledge-intensive IT/software consultancies, particularly in India's emerging economy. It indicates the current state of HR practices adopted for optimum utilization of knowledge resources and the importance of differentiation strategy for Indian knowledge-intensive IT/software firms.