Critical Enabler Research Articles

The Organizational Resources and Knowledge Management Capability: A Systematic Review

Competitive advantage and sustainability of business organizations can only be achieved when an organization identifies, boosts, and directs its resources toward building its capabilities. The literature identified knowledge management (KM) resources and capabilities as the most critical enablers of organizational performance and innovations. This systematic review identified KM resources (enablers) and their effects on KM capabilities. The review also went into the theories used in the empirical research on the problem. To achieve those goals, the authors summarized 27 quantitative, peer-reviewed studies found in well-known databases and published in the last 5 years between January 2014 and April 2019. The review revealed that the primary organizational KM resources are culture, people, leadership, organization structure, resources, capabilities, strategy and technology. To these, the review added some factors, which are social factors, organizational knowledge and organizational characteristics. The study is significant in finding the most common variables or factors in business KM and their implication of them for enhancing knowledge capability and organizational performance.

Exploring the experiences and perspectives of women on oral health during pregnancy: A qualitative study in Tasmania, Australia.

To explore women's oral health experiences and barriers to dental care and identify potential strategies to improve oral health during pregnancy. A qualitative descriptive study design was used. Purposively elected antepartum and postpartum women ≥18 years of age from Tasmania, Australia, were recruited using maximum variation sampling. Semi-structured interviews were conducted, and all qualitative data were analysed using an inductive thematic approach. Fifteen women were interviewed with a mean ± SD age of 32.3 ± 4.5 years. Three key themes were generated from the data that described women's perceived changes in their oral health during pregnancy; barriers to oral health care during and after pregnancy; and perceived strategies to improve access to care. Most women acknowledged the importance of maintaining good oral health but reported a decline in their oral health status during pregnancy. Women also identified several barriers to dental care, including treatment costs, competing maternal priorities, limited oral health knowledge and negative perceptions towards dentistry. The provision of preventative oral health care by ANC providers was also limited. Community awareness, patient education resources and assessment tools could support the promotion of oral health care. Women also perceived that interprofessional collaboration between antenatal and dental providers played a key role in promoting oral health. This study explored women's varied oral health experiences and perceptions during pregnancy and highlighted critical barriers and enablers to dental care. Policy-level strategies that promote interprofessional collaboration between antenatal and dental providers and expand dental care access are suggested to overcome barriers to oral health for women during pregnancy.

Critical contextual factors of sustainable online teaching: learning the lessons of pandemic times from physical education teachers’ and school administrators’ perspectives

ABSTRACT Background The COVID-19 pandemic has upended schooling around the world and prompted a shift to online teaching and learning, including in primary (elementary) physical education (PE). PE is typically characterized by physicality and close proximity to others, the shift to online delivery has drastically changed this landscape and brings significant consequences for the future of PE in schools. Teacher and school administrator perspectives on the critical enablers to effective online delivery of primary PE during the pandemic can help inform the development of sustainable online education that enhances teaching, learning, and professional development in the post-COVID context. Purpose Informed by the job characteristics theory (JCT) and social-ecological model (SEM), this study aimed to explore the contextual factors contributing to effective and potentially sustainable online teaching from the perspectives of PE teachers and school administrators. Methods Semi-structured interviews with a total of 16 teaching staff including two principals, two vice principals, and 12 PE teachers were conducted. Taking a combined inductive/deductive thematic approach, the data analysis was underpinned by JCT to explore participants’ perception and motivation towards online teaching in PE that fostered its adoption, use, and enhancement during the pandemic. The SEM was also applied to enable further exploration of facilitators perceived by PE teachers and school administrators at the interpersonal, institutional, community and policy levels. Findings : Contextual factors of sustainable online teaching in PE at each level of SEM were identified by the participants. On the intrapersonal level, the motivating potential of the job and readiness appeared to enable the effective transition to online PE by teachers and schools. Of the interpersonal, community, institutional, and policy factors, parental engagement, reach out to the community, institutional leadership and policy making, influenced participant perceptions toward the effectiveness of online PE teaching and its potential sustainability. Conclusions Our findings revealed that the effectiveness of online teaching in primary PE setting during the COVID-19 pandemic was influenced by the interaction of factors across all levels of SEM. The lessons learned from PE teachers and school administrators have several implications for the potential sustainable development of online PE teaching. First, school leaders and policy makers need to understand the potential benefits of offering quality online PE lessons by schools during class suspension. Second, a review of teacher training programmes and professional development for the pedagogical shift in PE is warranted. Third, there is an equally pressing need to identify effective parental involvement practices that contribute to students’ home online PE learning during the school suspension.

Unlocking the Power of AI/ML in DevSecOps: Strategies and Best Practices

In today's rapidly evolving technological landscape, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into DevSecOps practices has emerged as a critical enabler for enhancing security, efficiency, and innovation in software development and deployment processes. This paper explores the strategies and best practices for harnessing the full potential of AI/ML within the DevSecOps framework. Beginning with an overview of DevSecOps principles and the role of AI/ML, the paper delves into specific strategies such as automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. Furthermore, it examines key challenges and considerations associated with implementing AI/ML in DevSecOps, including data privacy, algorithm transparency, and ethical implications. Through case studies and real-world examples, the paper illustrates how organizations can leverage AI/ML technologies to optimize their DevSecOps pipelines, mitigate security risks, and foster a culture of continuous improvement. By embracing these strategies and best practices, organizations can unlock the full potential of AI/ML to drive innovation, resilience, and agility in their DevSecOps initiatives.

Critical enablers and inhibitors for economic sustainability in the supply and demand of end-of-life vehicles in Malaysia: a SWOT analysis

Critical enablers and inhibitors for economic sustainability in the supply and demand of end-of-life vehicles in Malaysia: a SWOT analysis

Robust Dynamic Mode Decomposition Methodology for an Airfoil Undergoing Transonic Shock Buffet

Dynamic mode decomposition (DMD) is a data-driven technique widely used to analyze and model fluid problems, including transonic buffet flows. Despite its strengths, DMD is known to suffer from sensitivities to the selected settings and the characteristics of the data used. In this work, we closely examine the aforementioned sensitivities, identify possible pitfalls, and provide best practices for robustly performing DMD on a flow exhibiting transonic shock buffet. Specifically, we assess several DMD variants and test their sensitivity to the POD rank truncation and the sampling rate. A critical enabler to our analysis is a new presentation of the DMD algorithm as a modular framework consisting of five distinct steps. The tests also highlight the existing dangers of aliasing, when the sampling rate is too low. Finally, a list of practical recommendations and guidelines on how to accurately and robustly perform DMD on a transonic buffet flow is provided.

Deep Contextual Bandit and Reinforcement Learning for IRS-Assisted MU-MIMO Systems

The combination of multiple-input multiple-output (MIMO) systems and intelligent reflecting surfaces (IRSs) is foreseen as a critical enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system. Both approaches aim to maximize the system sum-rate for every channel realization. The first proposed solution is a novel contextual bandit (CB) framework with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions, and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is appropriately met. Both proposals perform remarkably better than state-of-the-art heuristic methods in scenarios with high multi-user interference.

Architecting Descriptive Models for MBSE

AbstractModel‐Based Systems Engineering (MBSE) is becoming increasingly recognized as a critical enabler for significantly improving the effectiveness and efficiency of systems engineering. While MBSE is seeing significant growth in its application to complex system development programs, the growth of MBSE adoption and realized value is often stymied by the growth of technical debt in the descriptive models that serve as the foundation of knowledge capture and communication in an MBSE environment. Just as a good system architect or software architect makes key decisions early in the life of a system or software product to maximize the likelihood of stakeholder satisfaction throughout its life, a good model architect can provide that same invaluable function for descriptive models. The methods and practices of system and software architecting can be readily adapted to address the technical debt growth problem often seen in the development of descriptive models. This paper describes the emerging discipline of “model architecting,” comparing and contrasting it with system and software architecting practices. The paper describes a representative set of model architecting patterns that illustrate model architecting principles, then identifies some of the key roles, responsibilities, and competencies required of the model architect.

Towards AIOps enabled services in continuously evolving software‐intensive embedded systems

AbstractContinuous deployment has been practiced for many years by companies developing web‐ and cloud‐based applications. To succeed with continuous deployment, these companies have a strong collaboration culture between the operations and development teams. In addition, these companies use AI, analytics, and big data to assist with time‐consuming postdeployment activities such as continuous monitoring and fault identification. Thus, the term AIOps has evolved to highlight the importance and difficulty of maintaining highly available applications in a complex and dynamic environment. In contrast, software‐intensive embedded systems often provide customer product‐related services, such as maintenance, optimization, and support. These services are critical for these companies as they provide significant revenue and increase customer satisfaction. Therefore, the objective of our study is to gain an in‐depth understanding of the impact of continuous deployment on product‐related services provided by software‐intensive embedded systems companies. In addition, we aim to understand how AIOps can support continuous deployment in the context of software‐intensive embedded systems. To address this objective, we conducted a case study at a large and multinational telecommunications systems provider focusing on the radio access network (RAN) systems for 4G and 5G networks. The company provides RAN products and three complementing services: rollout, optimization, and customer support. The results from the case study show that the boundaries between product‐related services become blurry with continuous deployment. In addition, product‐related services, which were conducted in sequence by independent projects, converge with continuous deployment and become part of the same project. Further, AIOps platforms play an important role in reducing costs and increasing postdeployment activities' efficiency and speed. These results show that continuous deployment has a profound impact on the software‐intensive system's provider service organization. The service organization becomes the connection between the R&D organization and the customer. In order to cope with the increased speed of releases, deployment and postdeployment activities need to be largely automated. AIOps platforms are seen as a critical enabler in managing the increasing complexity without increasing human involvement.

Circular economy initiatives are no guarantee for increased plastic circularity: A framework for the systematic comparison of initiatives

Plastic plays a prominent role within circular economy, with many stakeholders promoting initiatives to increase plastic circularity during the manufacturing, use and end-of-life phases. Despite well-meant intentions, many initiatives are characterized by a lack of compliance with basic circular economy principles, implementation barriers, and limited effects on the intended long-term plastic circularity. This study provides a systematic evaluation framework for comparison of plastic initiatives, based on 17 criteria addressing key aspects of plastic circularity. A three-level likelihood ranking approach is applied to analyse the impact of 54 initiatives targeting plastic circularity in a European context. It was found that relatively few of these initiatives were readily implementable without considerable investments, e.g. in new waste management and recycling technologies, and changes in plastic production and product design. The results clearly suggest that current suggestions for circular economy initiatives targeting plastic may have limited effect and not lead to the intended impacts without the support of new regulations and change in plastic demand and consumption. The study stresses the importance of synergies and cooperation between stakeholders across the value chain to reach plastic circularity. The framework offers a consistent basis for decision-makers to identify critical barriers and enablers in relation to plastic circularity characteristics, but the approach may also be applied to other topic areas.

Investigating the Causal Relationships among Enablers of the Construction 5.0 Paradigm: Integration of Operator 5.0 and Society 5.0 with Human-Centricity, Sustainability, and Resilience

The Construction 5.0 paradigm is the next phase in industrial development that aims to combine the skills of human experts in partnership with efficient and precise machines to achieve production solutions that are resource-efficient and preferred by clients. This study reviewed the evolution of the Construction 5.0 paradigm by defining its features and diverse nature. It introduced the architecture, model, and system of Construction 5.0 and its key enablers: Operator 5.0, Society 5.0, human-centricity, sustainability, and resilience. The study used the SEM method to evaluate the research model and investigate the causal relationships among the key enablers of the Construction 5.0 paradigm. Nine vital hypotheses were proposed and assessed comprehensively. The critical enablers’ variables were measured to examine the constructs’ reliability and validity. The key findings showed that Construction 5.0 prioritizes collaboration between humans and machines, merges cyberspace with physical space, and balances the three pillars of sustainability (economy, environment, and society), creating a relationship among Operator 5.0, Society 5.0, human-Ccentricity, sustainability, and resilience. The study also discussed the limitations and challenges and offered suggestions for future research. Overall, Construction 5.0 aims to achieve sustainable development and become a robust and resilient provider of prosperity in an industrial community of a shared future. The study expects to spark debate and promote pioneering research toward the Construction 5.0 paradigm.

The sustainability agenda and its implications for post-trade technology adoption

Environmental, social and governance (ESG), referring to the process of integrating ESG factors into investment decisions, and sustainable and impact investing, referring to the intent to deliver an environmental or social outcome on top of financial performance, are growing very rapidly. BNY Mellon and Aviva Investors reflected on implications for post-trade technology adoption. Rapid growth in demand is accompanied by increased expectations from a range of parties including regulators with a focus on disclosures and labelling, international institutions seeking greater alignment and lobbying groups asking for more data. All of this is affecting data to be sourced, controlled and reported, and reporting is complicated by lack of consensus on ESG definitions and difficulty in comparing data and incorporating investors' preferences across jurisdictions. Each party in the post-trade custody chain depends on reference data and needs access to the same accurate sustainability ESG reference data. Missing, different and inaccurate reference data creates errors and discrepancies. Sustainability data acquisition is thus becoming an emerging area of focus for post-trade servicing teams, particularly finding, formatting, correcting and monitoring updates and maintaining asset coverage, creating a shift in operating model. Technology is a critical enabler given the growing volume of data, lack of a single ESG definition and the need to prove product claims and support clients' preferences throughout the custody chain

Deep-Learning-Enabled Predictive Maintenance in Industrial Internet of Things: Methods, Applications, and Challenges

With the great evolution of human society from the information era to the smart automation era, intelligent production and maintenance have become the core orientation of Industry 4.0. Data-driven intelligent predictive maintenance (IPdM) offers potential solutions to satisfy the urgent demand of predicting impending failures and mitigating unplanned downtime of industrial machinery, and is envisioned as a critical enabler for future industrial Internet of Things. In this article, we provide a comprehensive survey of deep learning (DL)-based IPdM for the researchers and practitioners who focused on the promotion of fault diagnosis/prognosis. First of all, four mainstream algorithms of DL are reviewed around the specific IPdM applications of recent five years. Some prominent discussions and insights on the DL-based IPdM are given here in terms of data characteristics and model performance. Particularly, as an important contribution, a new industrial dataset of dual-bearings rotating machinery is published for opening research to address one of the main challenges for the development of DL-based IPdM, i.e., the shortage of high quality industrial datasets. Compared with current datasets, our dataset is multisensory, which is closer to reality and more suitable for building advanced DL-based IPdM models. We also give a DL-based case study to demonstrate how to build a predictive model for maintenance decisions based on this dataset. Finally, potential research trends and challenges are also discussed for enhancing the intelligence of IPdM in the industrial scenarios.

Quantum for 6G communication: A perspective

AbstractIn the technologically changing world, the demand for ultra‐reliable, faster, low power, and secure communication has significantly risen in recent years. Researchers have shown immense interest in emerging quantum computing (QC) due to its potentials of solving the computing complexity in the robust and efficient manner. It is envisioned that QC can act as critical enablers and strong catalysts to considerably reduce the computing complexities and boost the future of sixth generation (6G) and beyond communication systems in terms of their security. In this study, the fundamentals of QC, the evolution of quantum communication that encompasses a wide spectrum of technologies and applications and quantum key distribution, which is one of the most promising applications of quantum security, have been presented. Furthermore, various parameters and important techniques are also investigated to optimise the performance of 6G communication in terms of their security, computing, and communication efficiency. Towards the end, potential challenges that QC and quantum communication may face in 6G have been highlighted along with future directions.

A Literature Review on the Application of Digital Technology in Achieving Green Supply Chain Management

Digitization and greening have become the characteristics of social and economic development. Digital technology, as a critical enabler of green supply chain management, has been widely considered and recognized by academia and business circles. With the advent of the Industry 4.0 era and the rapid development of digital technology, this emerging field of technology is constantly being updated, and so the academic research in this field is increasing but has yet to reach saturation. We systematically reviewed 144 relevant papers published in the last 14 years. We used qualitative analysis to classify, summarize and analyze the literature in two dimensions (i.e., digital technologies and green supply chain practices). Then, we continued the classification from each dimension. According to the basic characteristics, there are five digital technologies: the Internet of Things, big data, cloud computing, blockchain, and artificial intelligence. The green supply chain is divided into green procurement, green production, green consumption, and green logistics according to the essential practices of the supply chain. This study explores which digital technologies are needed in a green supply chain. The study also discusses how these technologies can reduce the input of resources and energy and the emission of pollutants, finally improving the operating efficiency of the green supply chain, and achieving economic, social, and environmental benefits.

Modeling key enablers influencing FinTechs offering SME credit services: A multi-stakeholder perspective.

The study models inter-relationship among key enablers that influence the growth of FinTechs that offer credit services to small and medium enterprises (SMEs). It focuses on emerging market of India, which is the world's third-largest FinTech centre. It employs Grey DEMATEL method to measure the cause-effect relationship based on the assessment given by FinTech practitioners, experts, policymakers, and investors. The results show that credit demand by SME borrowers, availability of alternate data sources, and Covid-19 are the critical enablers that exercise strong impact on FinTech system. Collaboration between FinTechs and traditional financial institutions, end-to-end financial solutions, and scalability of business operations are recognized as critical dependents that are hugely affected by others. The study recommends policymakers to foster collaborative environment, strengthen digital data landscape, and improve financial literacy to develop FinTech sector. It recommends practitioners to focus on data security and to offer end-to-end financial solutions to its SME borrowers.

Exploring knowledge management enablers for blockchain-enabled food supply chain implementations

PurposeWith the advent of new technologies, the integration of blockchain-enabled food supply chain (FSC) implementations is on the rise. Considering the multilateral, comprehensive and complex nature of the whole blockchain-enabled FSC implementation process, understanding knowledge management (KM) practices will add value. Prior literature shows that only a few studies have focused on this topic. As such, this study aims to identify and establish key KM enablers for blockchain-enabled FSC implementations.Design/methodology/approachThis study conducted a qualitative research approach. By conducting 22 in-depth interviews with experienced blockchain implementation experts in FSC contexts, this study provides interesting insights for academics and practitioners.FindingsThe results of the analysis highlighted eight critical KM enablers that directly influence blockchain-enabled FSC implementations. They include external enablers (i.e. regulation and market competition) as well as internal enablers (i.e. people – organizational learning, strategy and leadership, culture, information technology, organizational infrastructure, processes and activities).Originality/valueThis study is one of the few studies that identify KM enablers for blockchain-enabled FSC implementations. Considering the novelty of decentralized blockchain implementations in FSC and its importance in transforming silo-based knowledge exchange to a decentralized operational structure, this study attempts to identify factors that increase the efficiency of blockchain implementations in FSC contexts.

All Hands on Deck: We Need Multiple Approaches To Uncover the Neuroscience behind the Opioid Overdose Crisis.

Opioid use disorder (OUD) affects millions of people throughout the United States, yet there are only three Food and Drug Administration-approved pharmacological treatments. Though these treatments have been shown to be effective, the number of overdose deaths continues to rise. The increase of fentanyl, fentanyl analogs, and adulterants in the illicit drug supply has further complicated treatment strategies. Preclinical researchers strive to model OUD to better understand this complicated disorder, and this research is a critical enabler for the development of novel treatments. As a result, there are many different preclinical models of OUD. Often, researchers form strong opinions on what they believe to be the "best" model to mimic the human condition. Here, we argue that researchers should be supportive of multiple models to promote new perspectives and discoveries and always consider the trends in human opioid use when designing preclinical studies. We describe the benefits of contingent and noncontingent models as well as models of opioid withdrawal and how each of these can help illuminate different components of OUD.

Nomads and Warlords, Chadian Forces in African Peace Operations

Abstract Despite criticism of the United Nations (UN) as peacekeepers “hiding behind sandbags,” by the former president of Chad, the Chadian military has become a critical enabler of African-led and UN peace operations. This paper posits that the effectiveness of the Chadian forces stems from refined and modified nomad and warlord structures and attributes used during Chad’s various conflicts to build and improve its national army. This has allowed the Chadian regime to exercise and project power, thus, producing one of Africa’s most effective forces for current conflicts and challenges. Thus, Chad’s military leadership reflects a trend of states that use military prowess to project force, while maintaining international partnerships with permanent members of the unsc (the US and France), UN peacekeeping missions and African ad hoc security initiatives. Finally, the paper examines the implications of this trend for the evolving nature of African Peace and Security Architecture.

Hybrid Electric Vehicle Engine Operation and Engine Oil Degradation: A Research Approach

<div>Hybrid electric vehicles (xHEV) are a critical enabler to fulfil the most recent CO<sub>2</sub> and fuel economy requirements in key markets like North America, China, and Europe [<span>1</span>, <span>2</span>]. Different levels of hybridization exist; the main differentiator is the power of the electric system and battery capacity. Increased electrical power enables the vehicle to run more often in electric mode and recuperate energy from braking, which enhances the saving potential [<span>3</span>]. Mild (MHEV) and plug-in hybrid vehicles (PHEV) impose different duty cycles on the engine compared to a conventional powertrain, potentially altering the degradation mechanisms of the lubricant, and challenging the basis on which the lubricant should be condemned [<span>4</span>]. The biggest concerns are water and fuel dilution [<span>5</span>], which promote corrosion and can form emulsions [<span>6</span>]. This may result in so-called white sludge formation (a thick and creamy emulsion) which can deposit inside the engine on colder surfaces, potentially blocking pipes and breather hoses [<span>6</span>]. White sludge deposits on the oil filler cap can become visible to the vehicle operator and may be a reason for concern. Many original equipment manufacturers (OEMs), and their customers, need advice in defining the important oil parameters for the oil to be fit for purpose. If oil and additive companies are to respond to these challenges, an increased awareness and understanding of oil degradation in modern vehicle platforms is required. In this work, we have investigated the operating conditions in different hybrid vehicles and their impact on the engine oil. First, a chassis dynamometer (CD dyno) test program was conducted to understand how three different concepts influence engine operation, specifically the engine oil temperature and the number of stop/start events. Second, engine dyno testing was designed to replicate a worst-case scenario, extrapolating some of the observations from CD testing, to investigate the effect of an extreme drive cycle on the engine oil degradation and contamination. Finally, an analysis of the chemical and physical properties of these engine test drain oils, and the resulting impact on wear protection and engine cleanliness, was undertaken to understand the risks associated with worst-case scenario xHEV operation.</div>