Knowledge Research Articles

Impact of Human Capital and Structural Capital on Competitive Advantage: Evidence from Indonesian Provincial Government Institutions

Purpose: This study investigates the influence of human capital, structural capital, and knowledge management on competitive advantage within the North Sulawesi Provincial Government, Indonesia. It aims to understand whether knowledge management mediates the relationship between human capital, structural capital, and competitive advantage in the context of public sector organizations. Theoretical Reference: The research is grounded in the Knowledge-Based View (KBV) theory, which postulates that knowledge is a critical resource for competitive advantage. It also explores the role of intellectual capital (human and structural capital) as sources of knowledge and their interplay with knowledge management. Method: A quantitative approach was utilized, involving 147 public officers from seven departments under the North Sulawesi Provincial Government. Data were collected through questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 3.0 software. Results and Conclusion: The findings indicate that: Human capital positively impacts competitive advantage. Structural capital does not directly influence competitive advantage but has a significant effect through the mediation of knowledge management. Knowledge management positively affects competitive advantage and mediates the relationship between structural capital and competitive advantage. However, it does not mediate the relationship between human capital and competitive advantage.These results highlight the critical role of knowledge management in leveraging structural capital for competitive advantage in public sector organizations. Implications of Research: Theoretical Implications: The study contributes to the literature on intellectual capital and competitive advantage in public sector contexts, providing novel insights into the mediating role of knowledge management. Practical Implications: Policymakers should focus on enhancing human capital, improving structural capital, and fostering effective knowledge management processes to boost the competitive advantage of public sector organizations. Originality/Value: This research addresses a gap in understanding competitive advantage within Indonesian public institutions by examining intangible resources and knowledge management. It provides a unique perspective on public sector competitiveness, particularly in the context of regional government institutions in North Sulawesi.

Unveiling the Essence of Gastronomy: A Data-Driven Exploration of Culinary Expertise in the Nordic Region

The science of gastronomy is a context-sensitive and qualitative application of human knowledge. While qualitative content analysis is a widely recognized research method, it is time-consuming, and its results heavily depend on the analyst’s expertise. This study addresses these challenges by employing automated content analysis (ACA), including tools like VOSViewer and AntConc, alongside qualitative methods from systemic functional linguistics (SFL). By analyzing 25 interviews with various professionals in gastronomy, the research aims to reduce subjectivity in interpreting data and uncover the essence of being a gastronomic chef. The findings highlight the complexity of a chef’s role, emphasizing creativity, collaboration, and innovation as key factors in delivering exceptional service and crafting the ultimate dining experience. Understanding a chef’s knowledge is crucial for enhancing service quality and comprehending consumer behavior, which is vital for service development.

Transforming Breast Cancer Detection: AI as a Catalyst for Enhanced Clinical Outcomes

A revolutionary development in contemporary healthcare, especially in oncology, is the application of artificial intelligence (AI) to breast cancer diagnosis. The use of AI-powered diagnostic tools in breast cancer screening programs is examined in detail in this article, which shows notable gains in workflow efficiency, accuracy, and early detection. Along with quantitative performance indicators and system reliability measures, it examines the technological framework, which includes deep learning architectures and integration workflows. Through a thorough analysis of data processing issues, fixes, and clinical implications, the article shows how AI enhances radiologists' skills without replacing human knowledge. It also discusses future technological advancements and scaling issues, emphasizing the possibility of integrating multi-modal imaging and improving predictive capacities. This approach makes a strong case for wider deployment across healthcare systems and is a blueprint for future AI integration in medical diagnostics.

Availability and use of educational databases for effective teaching and learning by academic staff

Background of the Study: Educational database also known as academic database or online databases as collection of electronic information resources produced by publishers on various disciplines of human knowledge. These databases, plays a pivotal role in streamlining operations enhancing educational quality and ensuring efficient communication in institutions of learning at all levels. Purpose: The purpose of the study is to determine the availability of educational databases in Usmanu Danfodiyo University, Sokoto as well as determine the extent to which these resources are utilized by academics in the study area. Method: Quantitative research approach using descriptive design was used involving 254 academics in Usmanu Danfodiyo University, Sokoto as sample respondents. Findings: Findings of the study shows that there are several kinds of educational databases in the university. It further revealed that the most frequently used databases by academics in Usmanu Danfodiyo University, Sokoto are Pavilion, HINARI, Elsevier, Science Direct and DOAJ amongst others with a high extent of mean score ranging between 2.75-3.07 and SD= of .82-.96. These are above the 2.50 accepted average ranges. On extent of use, the findings revealed a high extent of the mean of 2.76-2.95 and SD= of .85-.1.0 which are both above the 2.50 accepted average mean. The Cluster of mean of 2.83 shows the extent of utilization of databases. Conclusion: The study concludes that the use of educational databases by academic staff contributed immensely towards effective teaching and learning in Usmanu Danfodiyo University, Sokoto.

Zika Virus Model with the Caputo–Fabrizio Fractional Derivative

In this article, we examine a deterministic Zika virus model that takes into account the vector and sexual transmission route, in the absence of disease-induced deaths, symmetrically observing the impact of human knowledge and vector control. In order to construct the model, we suppose that the Zika virus is first spread to humans through mosquito bites, and then to their sexual partner. In this article, we conduct analytical studies which often begin by proving the existence and uniqueness of solutions for the Zika virus model using the fractional derivative from the Caputo–Fabrizio derivative. Then, the uniqueness of the solution is investigated. After that, we also identify under which circumstances and symmetry the model provides a unique solution.

UNet-like transformer for 1D soil stratification using cone penetration test and borehole data

Subsurface stratification is crucial for the construction safety of underground projects. The one-dimensional (1D) soil stratification aims at identifying segmentation points that separate soil strata. Current engineering practice mainly requires human judgement, which is time-consuming, labour-intensive, and heavily relies on domain expertise. Other probabilistic methods, such as Bayesian approaches, usually involve complex expressions. With the advent of artificial intelligence, deep learning has emerged as a powerful tool in various domains. The UNet, as a typical convolutional neural network, has been extensively utilized for its superior performance in segmentation tasks, but struggles to capture global and long-range semantic information due to the locality of convolution operations. To realize intelligent and automatic 1D soil stratification, this paper introduces a UNet-like Transformer (ULTra) that integrates multiple data sources, including cone penetration test and borehole data, to incorporate prior knowledge. The architecture features a multi-level Transformer with shifted windows in both the encoder and decoder to extract context features and restore spatial resolution, respectively. Experimental results demonstrate that the ULTra outperforms other UNet variants, particularly in detecting minor textures and local details, underscoring the benefits of integrating Transformers into a standard UNet. Case studies indicate that compared with probabilistic methods, the ULTra enables automatic 1D soil stratification using original exploration data with less human intervention, which is fast, effective, and could be continuously improved through interaction with human knowledge, thus streamlining the intelligent data analysis.

Federated Meta Embedding Concept Stock Recommendation

Mining relevant stocks given a trending topic/concept in capital markets is an application with significant economic and societal impacts. Previous concept stock recommendation system mines concept stocks only from public social media like financial news. On stock forums, investors discuss emerging concepts and stocks by using forum comments, which are unneglectable resources to capture trending concept stocks accurately and timely. However, the comment data from a single forum is insufficient to build a high-quality recommendation system. The forums are data silos protected by privacy regulations, and their comments are still underutilized. In this paper, we propose a federated concept stock recommendation baseline and an optimized method that both leverage the private forum comments and public social media without compromising privacy regulations. Our baseline, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , Federated Meta Embedding (FedME), is built upon the federated learning framework and learns a concept-stock embedding jointly from private and public data. Our optimized method, Federated Graph Meta Embedding (FedGME), improves FedME by using a graph to combine two sources of embeddings and additional human experts' concept-stock knowledge. Empirically, the experiments on two concept stock datasets show that FedME and FedGME substantially improve the performance of recommendation. Our methods provide practical guidance on privacy-preserving FinTech applications.

Analyzing Trends in the Medical Community During the Spanish Flu Pandemic in Japan with the Comprehensive Knowledge of Humanities and Sciences: A Case Study of the Medical Journal, The Japan Medical World

This study aimed to examine the trends in the medical community in Japan during the Spanish flu pandemic by analyzing The Japan Medical World (JMW), a medical journal of modern Japan. The analysis included 3,856 articles published in issues Nos.228–279, from January 1918 to March 1919. Three key issues emerged from this analysis. First, the tone of the JMW’s discourse may have been influenced by the prevailing medical situation or may have placed emphasis on specific aspects of the situation. Second, the JMW and the medical community in Japan initially neglected the influenza pandemic until the autumn of 1918, when the Spanish flu became widespread in Japan. Third, complex factors, such as academic factionalism and conflicts within the pharmaceutical industries, influenced the Japanese medical community, particularly the bacteriological scientific community during the Spanish flu pandemic. Therefore, “a comprehensive approach” to understanding the period, including not only medical knowledge but also critical reading skills of humanities and social sciences, is indispensable for analyzing the trends in the Japanese medical community during the Spanish flu pandemic. Attention should be paid to these factors when examining the trends in the medical community in Japan at that time.

Exercise physiology degrees in the United States: an update on secular trends.

The number of degrees conferred by postsecondary institutions in an academic field reflects institutional priorities, public interest in a domain of human knowledge, and overall societal and economic demand for certain skills and abilities. Public postsecondary institutions, which are obligated to serve the public interest, and professional organizations, which are meant to reflect the values and interests of their members, should monitor degrees conferred, in part, to understand the career pathways of their graduates or members. The current report describes secular trends in degrees conferred in exercise physiology in the United States since 2002-2003. Data were obtained from spreadsheets published by the National Center for Education Statistics. The number of bachelor's degrees conferred in exercise physiology plateaued starting in 2017-2018 before increasing again in 2020-2021. In 2021-2022, 31,066 bachelor's degrees were conferred in exercise physiology, representing the highest yearly total on record (1.5% of all U.S. bachelor's degrees). The increase has been driven by female graduates (2021-2022: 58.9% women). The number of male graduates started plateauing in 2016-2017. The number of master's degrees conferred has continued to increase (2021-2022: 51.6% women), and doctorate degrees have numbered between 245 and 300 per year since 2013-2014 (2021-2022: 52.2% women). Moving forward, exercise physiology organizations are encouraged to monitor secular trends in degrees conferred and include these data in large-scale graduate destination reports. The plateauing of male exercise physiology graduates over the past 6 years and the significantly lower proportion of male than female graduates averaged across all academic fields warrant continued discussion.NEW & NOTEWORTHY The field of exercise physiology has experienced substantial growth at postsecondary institutions in the United States over the past 20 years. Growth has slowed in recent years, but the number of bachelor's degrees conferred in the field reached a record high in 2021-2022. The increase in bachelor's degrees conferred has been driven largely by increased numbers of female graduates (58.9% female). The number of male graduates plateaued starting in 2016-2017.

Editorial: Occupational therapy and occupational science: the evolving, dynamic and reciprocal research alliance for advancing human occupation knowledge and evidence

Editorial: Occupational therapy and occupational science: the evolving, dynamic and reciprocal research alliance for advancing human occupation knowledge and evidence

Large Language Model Enhanced Logic Tensor Network for Stance Detection

Social media platforms, rich in user-generated content, offer a unique perspective on public opinion, making stance detection an essential task in opinion mining. However, traditional deep neural networks for stance detection often suffer from limitations, including the requirement for large amounts of labeled data, uninterpretability of prediction results, and difficulty in incorporating human intentions and domain knowledge. This paper introduces the First-Order Logic Aggregated Reasoning framework (FOLAR), an innovative approach that integrates first-order logic (FOL) with large language models (LLMs) to enhance the interpretability and efficacy of stance detection. FOLAR comprises three key components: a Knowledge Elicitation module that generates FOL rules using a chain-of-thought prompting method, a Logic Tensor Network (LTN) that encodes these rules for stance detection, and a Multi-Decision Fusion mechanism that aggregates LTNs’ outputs to minimize biases and improve robustness. Our experiments on standard benchmarks demonstrate the effectiveness of FOLAR, showing it as a promising solution for explainable and accurate stance detection. The source code will be made publicly available to foster further research.

Human prior knowledge estimation from movement cues for information-based control of mobile robots during search

Robotic search often involves teleoperating vehicles into unknown environments. In such scenarios, prior knowledge of target location or environmental map may be a viable resource to tap into and control other autonomous robots in the vicinity towards an improved search performance. In this paper, we test the hypothesis that despite having the same skill, prior knowledge of target or environment affects teleoperator actions, and such knowledge can therefore be inferred through robot movement. To investigate whether prior knowledge can improve human-robot team performance, we next evaluate an adaptive mutual-information blending strategy that admits a time-dependent weighting for steering autonomous robots. Human-subject experiments show that several features including distance travelled by the teleoperated robot, time spent staying still, speed, and turn rate, all depend on the level of prior knowledge and that absence of prior knowledge increased workload. Building on these results, we identified distance travelled and time spent staying still as movement cues that can be used to robustly infer prior knowledge. Simulations where an autonomous robot accompanied a human teleoperated robot revealed that whereas time to find the target was similar across all information-based search strategies, adaptive strategies that acted on movement cues found the target sooner more often than a single human teleoperator compared to non-adaptive strategies. This gain is diluted with number of robots, likely due to the limited size of the search environment. Results from this work set the stage for developing knowledge-aware control algorithms for autonomous robots in collaborative human-robot teams.

Human-CoMo: A Combination of Cognitive Knowledge Engineering and Data Modelling for Human-Cyber-Physical Systems in Intelligent Manufacturing

Many cyber-physical systems face the challenge of appropriately integrating domain-specific human expert knowledge into the cyber part to create a shared sphere of knowledge and intelligent interactions between humans and the semi-autonomous technical system. Cognitive engineering contributes methods and insights into higher-order cognition that help to embed human knowledge in an appropriate way. The original research introduces a novel transdisciplinary framework called Human-CoMo, which demonstrates a systematic modelling process, different human perspectives, and the integration of expert knowledge at multiple hierarchical levels. Fundamental principles inspired by human cognition, such as conceptual chunking and knowledge precision, are characterised. Furthermore, it is shown how knowledge hierarchies can be methodically reflected in appropriate data analysis and modelling levels for small and big data applications including artificial intelligence approaches. Combined knowledge- and data-based modelling approaches offer more flexibility to integrate the strengths of humans and technology in a complementary way. The cognitive foundations and their computational reflections are outlined for the technical example process electroplating from the field of materials and surface engineering. The possibilities and limitations of integrating human knowledge through formalisation and implications for future forms of human-machine interaction are discussed.

Humour, Transcendence, and Selfhood: An Essay on Lightness and Truth

AbstractThis article is concerned with a ‘lightness that is as far as possible from triviality’. It argues, firstly, that a connection can be drawn between comic perception and pictures of reality that entail transcendence, understood as an otherness at the heart of things that may be indirectly glimpsed but never fully grasped as the object of fixed knowledge. To picture transcendence as enigmatically near within immanence, rather than set abstractly over against it, introduces a sense of relativity and ambivalence into experience. This yields a comic lightness which is nonetheless philosophically serious because it has to do with truth. This has implications, secondly, for understanding selfhood. The essay looks at three accounts of transcendence in this context: the arguments of Socrates regarding the divine madness of eros in Plato's Phaedrus; the development of a more radical conception of transcendence via biblical sources in Philo and Augustine; and the application, via a reconceived Cartesian cogito, of a more moderated account of transcendence in the philosophy of Maurice Merleau‐Ponty to the themes of self‐knowledge and sincerity. In all cases, the affirmation of transcendence destabilises the ‘mundane’ world and human knowledge of it, and relocates the subject's centre of gravity outside themselves via a characteristic movement of release. In all of them, to grow towards truth requires a renunciation of self‐possession and secure self‐knowledge: the self must give itself over to transcendent otherness if it is to become what it more truly is. Both world and self increase in mystery in these different renditions, and this is where humour and lightness break in. The realisation of this mysteriousness is not a cause for despair, however, but the means by which one is freed from illusory claims to absolute knowledge (whether of world or self), and journeys towards truth, held now more lightly and with a greater sense of wonder.

ORAL HEALTH OF AGROINDUSTRY WORKERS: CHALLENGES OF THE 2030 AGENDA AND ESG CRITERIA, PROMOTING WELL-BEING AND INCLUSION TO INCREASE PRODUCTIVITY

Contemporary dentistry aims to replace private care with a health-related model. Agribusiness workers have difficulty accessing dental services. The dental care system has difficulties in acquiring, updating, referring, and monitoring patients. Artificial intelligence is a computer system that performs tasks that require human knowledge and skills, such as recognizing patterns and images, understanding open written and spoken language, perceiving relationships/connections, following decision algorithms, understanding concepts, and reasoning through the integration of new experiences, thus improving itself. This research aims to demonstrate the importance of a convolutional network for analyzing dental data that enables the diagnosis of different pathologies that may be found in agribusiness workers and the respective referral to services for recovery and control of the oral health of this target population. With the bibliometric review carried out, it was noticed that the construction and widespread use of convolutional networks in different dental specialties can contribute to the improvement of basic oral health care, in the private network and especially in the public network, thus benefiting agribusiness workers with an increase in quality of life, in oral health indexes and indicators, as well as in their productivity.

Adaptive human–machine theorem proving system

This paper presents a novel approach to constructing human–machine theorem proving systems. These systems integrate machine learning capabilities, human expert knowledge, and rigorous logical control for the effective construction and verification of proofs. The innovation of this approach lies in its openness: the user is given a tool for building such systems. Users can create theorem proving systems by selecting existing logical inference strategies or adding new ones in accordance with the provided interfaces or relationship agreements. The systems being built have a significant human--machine adaptive nature. During their operation, a meta-strategy is developed and trained based on the foundational elements of the existing strategies. The system is designed as a universal framework for managing various strategies, with the provision of a basic architecture and a library of strategies with the possibility of adding new ones. During the learning process, a system of structural characteristics is also accumulated and trained, on the basis of which a decision is made on the use of specific strategy elements at the next step. The presentation is conducted for the sequential calculus of minimal positive predicate logic as the most suitable for the deductive synthesis of programs and algorithms, for which it is proposed to use this approach.

Human knowledge and the derivation of universal laws from Quranic revelation

This article explores how the Quran provides a framework for deriving universal laws that guide human knowledge, behavior, and societal norms. It begins by raising three key questions: How does the Quran guide humans in deriving universal laws from revelation and the universe? What role does deduction play in understanding human behavior and societal norms as presented in the Quran? What are the differences between the “Sunnah of Allah”, the “Sunnah of the Messengers”, and the “Sunnah of past nations” in shaping human understanding of divine laws? The article explains that the Quran encourages humans to reflect on natural phenomena and human history to extract divine laws that govern the universe and human interactions. Through contemplation and deductive reasoning, individuals can derive legal rulings and societal norms from the Quranic text. Deduction, as explained by scholars like Ibn Manzur, involves extracting meanings from texts using reasoning and understanding, and it is considered a key method for understanding divine laws.

Credibility-based knowledge graph embedding for identifying social brand advocates.

Brand advocates, characterized by their enthusiasm for promoting a brand without incentives, play a crucial role in driving positive word-of-mouth (WOM) and influencing potential customers. However, there is a notable lack of intelligent systems capable of accurately identifying online advocates based on their social interactions with brands. Knowledge Graphs (KGs) offer structured and factual representations of human knowledge, providing a potential solution to gain holistic insights into customer preferences and interactions with a brand. This study presents a novel framework that leverages KG construction and embedding techniques to identify brand advocates accurately. By harnessing the power of KGs, our framework enhances the accuracy and efficiency of identifying and understanding brand advocates, providing valuable insights into customer advocacy dynamics in the online realm. Moreover, we address the critical aspect of social credibility, which significantly influences the impact of advocacy efforts. Incorporating social credibility analysis into our framework allows businesses to identify and mitigate spammers, preserving authenticity and customer trust. To achieve this, we incorporate and extend DSpamOnto, a specialized ontology designed to identify social spam, with a focus on the social commerce domain. Additionally, we employ cutting-edge embedding techniques to map the KG into a low-dimensional vector space, enabling effective link prediction, clustering, and visualization. Through a rigorous evaluation process, we demonstrate the effectiveness and performance of our proposed framework, highlighting its potential to empower businesses in cultivating brand advocates and driving meaningful customer engagement strategies.

The Future Of Human-Ai Collaboration In Software Development: A Narrative Exploration

Investigating how artificial intelligence technologies are changing conventional development methods and the role of human developers, this study investigates the changing dynamics of human-AI collaboration in software development. The research explores the opportunities and difficulties brought about by the incorporation of AI tools into software development processes using a thorough investigation of secondary data sources and the body of current literature. The results show that AI is going beyond simple automation in software development, creating a mutually beneficial partnership in which developers become strategic managers of AI-driven workflows and AI enhances human creativity and cognitive capacities. The transition from routine coding to higher-order problem-solving, the rise of new development models, and the increasing significance of ethical considerations in AI implementation are some of the major themes identified in the study. Important issues are also covered, such as the need for improved developer education in AI literacy, bias prevention, and transparency in AI decision-making. The study concludes that effective human-AI cooperation necessitates a well-rounded strategy that makes use of AI's processing capacity while preserving human supervision and originality. Among the suggestions include funding developer education, creating transparent AI systems, encouraging teamwork, and establishing moral standards for integrating AI. This study advances our knowledge of how artificial intelligence will influence software development in the future by indicating that the way forward is to establish an environment in which human knowledge and AI capabilities work in tandem to promote creativity and productivity in software development.

Efficient Robot Manipulation via Reinforcement Learning with Dynamic Movement Primitives-Based Policy

Reinforcement learning (RL) that autonomously explores optimal control policies has become a crucial direction for developing intelligent robots while Dynamic Movement Primitives (DMPs) serve as a powerful tool for efficiently expressing robot trajectories. This article explores an efficient integration of RL and DMP to enhance the learning efficiency and control performance of reinforcement learning in robot manipulation tasks by focusing on the forms of control actions and their smoothness. A novel approach, DDPG-DMP, is proposed to address the efficiency and feasibility issues in the current RL approaches that employ DMP to generate control actions. The proposed method naturally integrates a DMP-based policy into the actor–critic framework of the traditional RL approach Deep Deterministic Policy Gradient (DDPG) and derives the corresponding update formulas to learn the networks that properly decide the parameters of DMPs. A novel inverse controller is further introduced to adaptively learn the translation from observed states into various robot control signals through DMPs, eliminating the requirement for human prior knowledge. Evaluated on five robot arm control benchmark tasks, DDPG-DMP demonstrates significant advantages in control performance, learning efficiency, and smoothness of robot actions compared to related baselines, highlighting its potential in complex robot control applications.