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This paper seeks to evaluate the factors that contribute to congestion at port entrances, propose a comprehensive approach to managing port gates that addresses the factors causing traffic jams and assess the outcomes of resolving the issue through an optimal model for incoming container truck traffic.

The study employed a one-way ANOVA and a one-way MANOVA to examine the impact of congestion-causing factors on the waiting time of trucks in each lane at the entrance gate. The purpose of this was to comprehend the intricate issue and demonstrate the outcomes of the resolution. We used the identified factors that were causing congestion to develop a management strategy for the port gate. As part of this strategy, we implemented a policy where traffic flows in the opposite direction in certain lanes. The Simulation of Urban Mobility program introduced the microscopic traffic simulation model as a discrete event simulation.

The examination of variables influencing the congestion at the port entrance revealed that there were four factors contributing to the congestion: (1) the quantity of lanes; (2) the level of bookings; (3) the factors related to the traffic signal cycle and (4) the assignment of lane types. The one-way MANOVA analysis of the three factors yielded significant evidence for a single pair of interactions. (1) The factors to consider are the quantity of lanes, the level of booking and the assignment of lane types. If the entrance to the rear alley consists of two lanes with a width of 1.85 at the 50% capacity level, and if the critical value of the uneven queue coefficient is reached, it can result in a maximum reduction of the average waiting time by 15.02%.

This study is unique because it examines the surrounding environment and operational behavior of the port to identify how individual and group congestion factors interact. It uses various statistical tools to determine the allocation of the number of port entrances with a reversible lane policy and appointment level. Additionally, it analyzes the detailed results using microscopic traffic simulation modeling. The established foundational model can assist operators in simulating the queue length and mean waiting time of trucks for this specific waiting line in other ports with comparable entrance characteristics.

The paper proposes a framework for the successful deployment of Industry 4.0 (I4.0) principles in the aerospace industry, based on identified success factors. The paper challenges the perception of I4.0 being aligned with de-skilling and personnel reduction and instead promotes a route to successful deployment centred on upskilling and retaining personnel for future role requirements.

The research methodology involved a literature review and industrial data collection via questionnaires to develop and validate the framework. The questionnaire was sent to a purposive sample of 50 respondents working in operations, and a response rate of 90% was achieved. Content analysis was used to identify patterns, themes, or biases, and the data were tabulated based on specific common attributes. The proposed framework consists of a series of gates and criteria that must be met before progressing to the next gate.

The proposed framework provides a feedback mechanism to review minimum standards for successful deployment, aligned with new developments in capability and technology, and ensures quality assessment at each gate. The paper highlights the potential benefits of I4.0 implementation in the aerospace industry, including reducing operational costs and improving competitiveness by eliminating variation in manufacturing processes. The identified success factors were used to define the framework, and the identified failure points were used to form mitigation actions or controls for inclusion in the framework.

The paper provides a framework for the successful deployment of I4.0 principles in the aerospace industry, based on identified success factors. The framework challenges the perception of I4.0 as being aligned with de-skilling and personnel reduction and instead promotes a route to successful deployment centred on upskilling and retaining personnel for future role requirements. The framework can be used as a guideline for organizations to deploy I4.0 principles successfully and improve competitiveness.

This paper aims to address the challenge raised in the literature regarding whether and how digitalization supports a servitized new product development (NPD) process, considering the customer’s involvement from the early stage of the process.

Pragmatic constructivism (PC) has been adopted for conceptualizing the NPD process as the construction of a new reality. PC is the method theory used for interpreting the field evidence drawn from a qualitative case study carried out at a multinational company operating in the semiconductor industry.

This study shows how digitalization supports the alignment to the overarching topoi of the company servitization strategy by enabling the integration and merging of different organizational topoi during the NPD process.

This study is confined to a single-case study and context.

The results of this study are relevant for managers involved in the stage-gate product development of manufacturing companies, informing them on how the use of digital tools enables or hinders the progression of product development projects.

This paper contributes to the servitization literature by offering field evidence that demonstrates the importance for manufacturing firms of acquiring customer feedback from an early NPD phase. Another contribution is related to the literature on the role of digitalization in NPD processes, describing how digital tools give support during the different phases of the NPD process.

The purpose of this study is to investigate the influence of design for lean six sigma (DFLSS) on operational functions of the innovation management model by appraising an innovation management assessment framework.

An empirical approach for evaluating causal relationships among various constructs in the model phases that identify optimum pathways in achieving commercial success was adopted. A quantitative analysis of survey data were collected from large, medium and small organiations, including incubators in ANZ (Australia, New Zealand) and TMSV (Thailand, Malaysia, Sri Lanka and Vietnam).

The structural equation modelling recursive path analysis results of the model provide empirical evidence and pathways through the various constructs considered in the model. All these pathways lead to delivering optimum commercialization success (CS). Furthermore, DFLSS is confirmed as an enabler and has direct one-to-one and indirect influence on all the operational function constructs of the model including commercial success.

This study had a relatively small sample size of completed responses obtained from the population and a constrained ability to compare commercialization success (CS) between the two regions in the dataset. Future studies could be conducted on a global scale to increase responses.

The research findings enabled the development of important and practical guidelines for managers and innovation practitioners engaged in planning and management of innovation.

This research offers a holistic approach for integrating DFLSS with stage gate phases of innovation management assessment framework, supported by empirical evidence, to aid organizations in effectively managing the innovation process and achieving greater success in commercialization.

Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.

A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.

The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.

The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

This study aims to explore children’s cognitions about data flows online and their understandings of algorithms, often referred to as algorithmic literacy or algorithmic folk theories, in their everyday uses of social media and YouTube. The authors focused on children ages 8 to 11, as these are the ages when most youth acquire their own device and use social media and YouTube, despite platform age requirements.

Nine focus groups with 34 socioeconomically, racially and ethnically diverse children (8–11 years) were conducted in California. Groups discussed data flows online, digital privacy, algorithms and personalization across platforms.

Children had several misconceptions about privacy risks, privacy policies, what kinds of data are collected about them online and how algorithms work. Older children had more complex and partially accurate theories about how algorithms determine the content they see online, compared to younger children. All children were using YouTube and/or social media despite age gates and children used few strategies to manage the flow of their personal information online.

The paper includes implications for digital and algorithmic literacy efforts, improving the design of privacy consent practices and user controls, and regulation for protecting children’s privacy online.

Research has yet to explore what socioeconomically, racially and ethnically diverse children understand about datafication and algorithms online, especially in middle childhood.

This paper aims to improve the performance of approximate multiplier in terms of peak signal to noise ratio (PSNR) and quality of the image.

The paper proposes an approximate circuit for 4:2 compressor, which shows a significant amount of improvement in performance metrics than that of the existing designs. This paper also reports a hybrid architecture for the Dadda multiplier, which incorporates proposed 4:2 compressor circuit as a basic building block.

Hybrid Dadda multiplier architecture is used in a median filter for image de-noising application and achieved 20% more PSNR than that of the best available designs.

The proposed 4:2 compressor improves the error metrics of a Hybrid Dadda multiplier.

This paper aims to describe the product development process (PDP) of companies in the textile industry, seeking to understand the dynamics of their management from different actors along the production chain.

Qualitative empirical research adopted a multiple case studies design in five large Brazilian organizations, each representing a link in the production chain.

Textile PDP follows structured steps. However, it is still an informal process. The use of methodologies and tools for decision-making and control gates throughout the process is limited. Performance indicators do not cover all dimensions of the PDP since sales and profit are the main parameters for assessing projects. The predevelopment macro phase varies according to the product type and the company's business model, whereas the postdevelopment macro phase is nonexistent. PDP projects are executed through collective efforts of multiple departments in cross-functional teams, except for the commodities firms.

The study allows managers of Brazilian textile companies to understand the best practices in the PDP and those that require more attention, taking into account different business models and sectors of the production chain.

Our results contribute to the literature and practitioners by providing an overview of PDP management in the textile industry, covering its different production chain actors, types of projects and companies' characteristics.

Intellectual curiosity about innovation in philanthropic organizations has grown recently. This study aims to provide a thorough bibliometric analysis of the patterns and trends in the scientific literature on innovation in philanthropy.

Based on the Scopus database, a descriptive bibliometric analysis with a visualization tool (RStudio®) was used to assess the creation of 159 articles on innovation in philanthropic organizations.

This research finds a large number of papers on innovation in philanthropic organizations. According to this study, the USA has published more research than any other country. The Icahn School of Medicine has the most popular publications, followed by the Bill & Melinda Gates Foundation. According to the number of citations, the Journal of Business Ethics is the most prolific journal. However, according to the h-index, Corporate Reputation Review is the most important publication. Halme M is regarded as a prominent scholar. With 244 citations, the work of Kramer MR and Porter ME is the most referenced. “Philanthropy” is the most often used keywords category, followed by “innovation” and “social innovation”.

This study can serve as a useful reference for researchers conducting bibliometric research by offering information on the field’s famous authors. Furthermore, the outcomes of this study make it straightforward for researchers to seek extensive academic collaboration in this field.

To the best of the authors’ knowledge, this study is the first to present a pattern in research on innovation in philanthropic organizations.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.