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Despite the large amount of factual data on senior management's significant role in achieving corporate social responsibility (CSR), little effort has been devoted to shed light on the CSR decision-making process and the managerial mindsets that drive it. To fill this gap in CSR research, this study explores the CSR decision-making process and senior management mindsets regarding CSR, inductively prompted by the CSR literature and a qualitative study. This study offers a conceptual model of socially responsible decision-making based on the Doctus knowledge-based system. The model is empirically analyzed and verified through semi-structured interviews with CSR experts. Further, an inductive examination of the qualitative data using individual-level analysis revealed three different mindsets of senior management towards CSR: conformist, self-interested, and shareholder satisfaction/profit-driven mindsets. KEYWORDS: corporate social responsibility, decision-making model, mindsets., 1. INTRODUCTION Since the 1950s, corporate social responsibility (CSR) has been a topic of discussion in academia and industry (Fatima & Elbanna, 2022). Furthermore, the literature on business ethics emphasizes [...]

Background: Transtibial prosthetic sockets are often grouped into patella tendon bearing (PTB) or total surface bearing (TSB) designs, but many variations in rectifications are used to apply these principles to an individual’s personalised socket. Prosthetists currently have little objective evidence to assist them as they make design choices. Aims: To compare rectifications made by experienced prosthetists across a range of patient demographics and limb shapes to improve understanding of socket design strategies. Methodology: 163 residual limb surface scans and corresponding CAD/CAM sockets were analysed for 134 randomly selected individuals in a UK prosthetics service. This included 142 PTB and 21 TSB designs. The limb and socket scans were compared to determine the location and size of rectifications. Rectifications were compiled for PTB and TSB designs, and associations between different rectification sizes were assessed using a variety of methods including linear regression, kernel density estimation (KDE) and a Naïve Bayes (NB) classification. Results: Differences in design features were apparent between PTB and TSB sockets, notably for paratibial carves, gross volume reduction and distal end elongation. However, socket designs varied across a spectrum, with most showing a hybrid of the PTB and TSB principles. Pairwise correlations were observed between the size of some rectifications (e.g., paratibial carves; fibular head build and gross volume reduction). Conversely, the patellar tendon carve depth was not associated significantly with any other rectification, indicating its relative design insensitivity. The Naïve Bayes classifier produced design patterns consistent with expert clinician practice. For example, subtle local rectifications were associated with a large volume reduction (i.e., a TSB-like design), whereas more substantial local rectifications (i.e., a PTB-like design) were associated with a low volume reduction. Clinical implications: This study demonstrates how we might learn from design records to support education and enhance evidence-based socket design. The method could be used to predict design features for newly presenting patients, based on categorisations of their limb shape and other demographics, implemented alongside expert clinical judgement as smart CAD/CAM design templates. [ABSTRACT FROM AUTHOR]

Knowledge-based recommender systems have always had their privileged place among all Decision Support Systems (DSS), given their advantage on several points over other techniques. Our paper presents a framework implementing a hybrid form of Rule-Based Reasoning and Case-Based Reasoning (RBR-CBR), to address the rarely discussed domain of educational planning. The system has been tested and presented outstanding results with a high accuracy, which will benefit educational planners’ decision support. We have also developed a dedicated application for this project to visualize the results obtained.

Help desks play a crucial function in the information technology department by acting as the main point of solution to customers’ issues. The speed and efficiency of agent-centric help desks are diminished by the inability of a help desk agents to share their knowledge when they leave and the inability of agents applying past cases to solve present problem. In this study, a knowledge-based expert system for campus helpdesk request processing was presented. The presented system consists of a fusion of expert system and fuzzy inference system for campus helpdesk request processing. The fuzzy inference system is responsible for the rules while the expert system provided the interface for the user interactions. The presented system takes input queries from students through the interface of the expert system. The input queries are converted into fuzzy variables and the inference engine is then used to compare the fuzzy variables with the fuzzy rules in the knowledge base of the expert system. The database base of the expert system contains the solutions to past queries while the rule base is made up of the set of IF-THEN rules depicting the domain expert knowledge. The expert system will fire the rules that matches the input queries and deliver the answers to the queries through the expert system output interface. The evaluation results of the presented system when compared with previous research showed a better accuracy of 95.36767% in terms of its efficiency in providing solutions to requests relating to complaints from campus students.

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Purpose: This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care. Design/methodology/approach: The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities' knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system's knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach. Findings: Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities. Originality/value: An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development. [ABSTRACT FROM AUTHOR]

BackgroundTranstibial prosthetic sockets are often grouped into patella tendon bearing (PTB) or total surface bearing (TSB) designs, but many variations in rectifications are used to apply these principles to an individual's personalised socket. Prosthetists currently have little objective evidence to assist them as they make design choices.AimsTo compare rectifications made by experienced prosthetists across a range of patient demographics and limb shapes to improve understanding of socket design strategies.Methodology163 residual limb surface scans and corresponding CAD/CAM sockets were analysed for 134 randomly selected individuals in a UK prosthetics service. This included 142 PTB and 21 TSB designs. The limb and socket scans were compared to determine the location and size of rectifications. Rectifications were compiled for PTB and TSB designs, and associations between different rectification sizes were assessed using a variety of methods including linear regression, kernel density estimation (KDE) and a Naïve Bayes (NB) classification.ResultsDifferences in design features were apparent between PTB and TSB sockets, notably for paratibial carves, gross volume reduction and distal end elongation. However, socket designs varied across a spectrum, with most showing a hybrid of the PTB and TSB principles. Pairwise correlations were observed between the size of some rectifications (e.g., paratibial carves; fibular head build and gross volume reduction). Conversely, the patellar tendon carve depth was not associated significantly with any other rectification, indicating its relative design insensitivity. The Naïve Bayes classifier produced design patterns consistent with expert clinician practice. For example, subtle local rectifications were associated with a large volume reduction (i.e., a TSB-like design), whereas more substantial local rectifications (i.e., a PTB-like design) were associated with a low volume reduction.Clinical implicationsThis study demonstrates how we might learn from design records to support education and enhance evidence-based socket design. The method could be used to predict design features for newly presenting patients, based on categorisations of their limb shape and other demographics, implemented alongside expert clinical judgement as smart CAD/CAM design templates.

Traffic collisions pose a significant global concern, necessitating innovative solutions to enhance safety. Post-collision, it becomes crucial for the police to determine the responsibilities of the involved parties, distinguishing between criminal acts and non-criminal incidents. Insurance companies also rely on such investigations to compensate victims. Assessing responsibility after a crash is a complex task requiring advanced knowledge of road rules. For straightforward scenarios like crashes with traffic lights, decisions are fast and easy. However, in situations such as crashes without any traffic signs, expert knowledge is essential. Automating such tasks demands innovative approaches, representing a necessity for the future of the automobile and insurance industries. Despite these critical needs, there has been limited research in the domain. This paper introduces a system that is capable of detecting vehicle collision and implements an original responsibility assessment process to assess drivers responsibilities. This paper is an extended version of our previous work. In the previous work, the system supported only three types of head-on/angle crash scenarios without traffic lights within three weather conditions, in addition to those with traffic lights. This study presents the improved system which now accommodates six different types of head-on/angle crash scenarios without traffic lights within six harsh weather conditions, such as foggy and snowy days. Additionally, extensive experiments are conducted with results showing that the system performs better than its previous version, mainly during nighttime without traffic lights (up to 93% accuracy against up to 82.5% obtained previously). Moreover, through case studies and comparisons with existing research, the effectiveness and superiority of the system are demonstrated.

The textile and garment (T&G) industry is one of the key industries in the world. Vietnam is among the top five exporting countries of T&G globally. This industry is the second-largest export turnover in Vietnam which creates a large number of employment opportunities. Industry 4.0 requires the T&G industry to change. However, due to the limitation of resources and their situations, many enterprises in developing countries such as Vietnam are facing significant challenges in adapting their operation status as Industry 4.0. In addition, Industry 3.5 as a hybrid strategy proposed by Chien et al. (2017b) has been illustrated efficiently for the developing countries. To support these enterprises, especially in Vietnam toward Industry 3.5, this study first surveyed and analysed the status of the T&G enterprises in Vietnam. Then, a framework is proposed for enterprises toward Industry 3.5 properly and efficiently. The survey has shown that most of the T&G enterprises are under backward technology and operational methods. In addition, these companies employ huge number of laborers with low education. The proposed framework received positive feedback from the enterprises due to its low capital investment and suitability for their characteristics. [ABSTRACT FROM AUTHOR]

Aim/Purpose This research investigated the determinant factors influencing the adoption intentions of Chatsicum, a Knowledge-Based Chatbot (KBC) aimed at enhancing the species literacy of biodiversity students. Background This research was conducted to bridge the gap between technology, education, and biodiversity conservation. Innovative solutions are needed to empower individuals with knowledge, particularly species knowledge, in preserving the natural world. Methodology The study employed a quantitative approach using the Partial Least Square Structural Equation Modeling (PLS-SEM) and sampled 145 university students as respondents. The research model combined the Task-Technology Fit (TTF) framework with elements from the Diffusion of Innovation (DOI), including relative advantage, compatibility, complexity, and observability. Also, the model introduced perceived trust as an independent variable. The primary dependent variable under examination was the intention to use the KBC. Contribution The findings of this research contribute to a deeper understanding of the critical factors affecting the adoption of the KBC in biodiversity education and outreach, as studies in this context are limited. This study provides valuable insights for developers, educators, and policymakers interested in promoting species literacy and leveraging innovative technologies by analyzing the interplay of TTF and DOI constructs alongside perceived trust. Ultimately, this research aims to foster more effective and accessible biodiversity education strategies. Findings TTF influenced all DOI variables, such as relative advantage, compatibility, observability, and trust positively and complexity negatively. In conclusion, TTF strongly affected usage intention indirectly. However, relative advantage, complexity, and observability insignificantly influenced the intention to use. Meanwhile, compatibility and trust strongly affected the use intention. Recommendations for Practitioners Developers should prioritize building and maintaining chatbots that are aligned with the tasks, needs, and goals of the target users, as well as establishing trust through the assurance of information accuracy. Educators could develop tailored educational interventions that resonate with the values and preferences of diverse learners and are aligned closely with students' learning needs, preferences, and curriculum while ensuring seamless integration with the existing educational context. Conservation organizations and policymakers could also utilize the findings of this study to enhance their outreach strategies, as the KBC is intended for students and biodiversity laypeople. Recommendations for Researchers Researchers should explore the nuances of relationships between TTF and DOI, as well as trust, and consider the potential influence of mediating and moderating variables to advance the field of technology adoption in educational contexts. Researchers could also explore why relative advantage, complexity, and observability did not significantly impact the usage intention and whether specific user segments or contextual factors influence these relationships. Impact on Society This research has significant societal impacts by improving species literacy, advancing technology in education, and promoting conservation efforts. Species knowledge could raise awareness regarding biodiversity and the importance of conservation, thereby leading to more informed and responsible citizens. Future Research Future works should address the challenges and opportunities presented by KBCs in the context of species literacy enhancement, for example, interventions or experiments to influence the non-significant factors. Furthermore, longitudinal studies should investigate whether user behavior evolves. Ultimately, examining the correlation between species literacy, specifically when augmented by chatbots, and tangible conservation practices is an imperative domain in the future. It may entail evaluating the extent to which enhanced knowledge leads to concrete measures promoting biodiversity preservation. [ABSTRACT FROM AUTHOR]

More than 100 lichens have been found and identified in Thailand over the last century. Lichens perform useful environmental functions. Normally, they grow on trees, rocks and soils, in various forms and colours. They provide shelter and food for animals and plants. Lichens can also be used as indicators of air pollution, and some are researched for drugs, dyes, deodorants and extracts. This paper presents an ontology for lichens in Thailand, using lichen data from Khao Pluang, in Lopburi, Thailand. The database developed makes major contributions in the amount of knowledge on lichens that it stores, including the latitude and longitude of lichens at Khao Pluang specifically, and images of lichens. The system was implemented via web and mobile applications. The ontology knowledge consists of 6 main classes: 1) scientific name, 2) lichen type, 3) date found, 4) uses of the lichen, 5) function as an air pollution indicator, and 6) specific features of the individual lichen. Evaluation of the system was carried out by lichen and ontology specialists using the Software Usability Measurement Inventory (SUMI). [ABSTRACT FROM AUTHOR]

Labor and employment are important issues in social life. The demand for online job searching and searching for labor regulations in legal documents, particularly regarding the policy for unemployment benefits, is essential. Nowadays, each function has some programs for its working. However, there is no program that combines both functions. In practice, when users seek a job, they may be unemployed or want to transfer to another work. Thus, they are required to search for regulations about unemployment insurance policies and related information, as well as regulations about workers working smoothly and following labor law. Ontology is a useful technique for representing areas of practical knowledge. This article proposes an ontology-based method for solving labor and employment-related problems. First, we construct an ontology of job skills to match curriculum vitae (CV) and job descriptions (JD). In addition, an ontology for representing labor law documents is proposed to aid users in their search for legal labor law regulations. These ontologies are combined to construct the knowledge base of a job-searching and labor law-searching system. In addition, this integrated ontology is used to study several issues involving the matching of CVs and JDs and the search for labor law issues. A system for intelligent resume searching in information technology is developed using the proposed method. This system also incorporates queries pertaining to Vietnamese labor law policies regarding unemployment and healthcare benefits. The experimental results demonstrate that the method designed to assist job seekers and users searching for legal labor documents is effective. [ABSTRACT FROM AUTHOR]

Current studies on the artificial intelligence (AI) ethics focus either on very broad guidelines or on a very special domain. Therefore, the research outcome can hardly be converted into actionable measures or transferred to other domains. Potential correlations between various cases of AI ethics at different granularity levels are unexplored. To overcome these deficiencies, the authors designed a case-oriented ontological model (COOM) and a hyper-knowledge graph system (HKGS) for the research of collected AI ethics cases. COOM describes criteria for modelling cases by attributes from three perspectives: event attributes, relational attributes, and positional attributes on the value chain. Based on it, HKGS stores the correlation between cases as knowledge and allows advanced visual analysis. The correlations between cases and their dynamic changes on value chain can be observed and explored. In HKGS's implementation part, one of the collected ethics cases is used as an example to demonstrate how to generate a hyper-knowledge graph and to visually analyze it. The authors also anticipated how different practitioners of AI ethics, can achieve the desired outputs from HKGS in their diverse scenarios.

While recommender systems have shown success in many fields, accurate recommendations in industrial settings remain challenging. In maintenance, existing techniques often struggle with the “cold start” problem and fail to consider differences in the target population's characteristics. To address this, additional user information can be incorporated into the recommendation process. This paper proposes a recommender system for recommending repair actions to technicians based on an ontology (knowledge base) and a sequential model. The approach utilizes two ontologies, one representing failure knowledge and the other representing asset attributes. The proposed method involves two steps: i) calculating score similarity based on ontology domain knowledge to make predictions for targeted failures and ii) generating Top-N repair actions through collaborative filtering recommendations for targeted failures. An additional module was implemented to evaluate the recommender system, and results showed improved performance. [ABSTRACT FROM AUTHOR]

Maintenance methods have become automated and innovative, especially with the transition to maintenance 4.0. However, social issues such as coronavirus disease of 2019 (COVID-19) and the war in Ukraine have caused significant departures of maintenance experts, resulting in the loss of enormous know-how. As part of this work, we will propose a solution by exploring the knowledge and expertise of these experts for the purpose of sharing and conservation. In this perspective, we have built a knowledge base based on experience and feedback. The proposed method illustrates a case study based on the single excitation configuration interaction (SECI) method to optimally capture the explicit and tacit knowledge of each technician, as well as the theoretical basis, the model of Nonaka and Takeuchi. [ABSTRACT FROM AUTHOR]

This paper introduces an intelligent system to assess sustainability in small and medium-sized fashion businesses. The most relevant and common sustainability indicators were identified through literature review. Fuzzy logic was applied to handle uncertainty in evaluation. A web application was developed for use by interested companies and institutions. The usability of the system was evaluated using a questionnaire based on AttrakDiff, which showed that the system is intuitive, easy to use, novel, and attractive. The study results indicate that the system is potentially usable in evaluating and improving the sustainability of small and medium-sized enterprises in the fashion industry.

AbstractAn infant in the first 28 days following birth is referred to as a newborn baby. In Ethiopia, neonatal mortality is a serious problem that accounts for the lion’s share of under-five mortality. Diagnosis and treatment of infant disease need specialized medical resources with plenty of expert knowledge and experience. Globally and particularly in low-income countries, there is a lack of such professional which make the diagnosis and treatment more difficult. The goal of this paper is to design a knowledge-based system for the diagnosis and treatment recommendation of neonatal diseases by collaborating with the knowledge obtained from machine learning and health experts. Design science research approach has been employed as the overall research design, and the hybrid data mining process model is used to extract knowledge from the collected clinical dataset. To this end, three classification algorithms in WEKA tools, namely, J48, PART, and JRip, were considered. Then, a partial decision tree (PART) algorithm under 10-fold cross-validation achieved the highest performance result with an accuracy of 98.06% and the researchers decided to use the generated rules for the development of a knowledge-based system. Evaluation results show that the developed prototype registers 90.9% accuracy in system performance testing and 89.2% in user acceptance testing. In conclusion, the system is used as an assistant tool for healthcare experts and could be effective if it could be implemented

Software upcycling, a form of software reuse, is a concept that efficiently generates novel, innovative, and value-added development projects by utilizing knowledge extracted from past projects. However, how to integrate the materials derived from these projects for upcycling remains uncertain. This study defines a systematic model for upcycling cases and develops the Sharing Upcycling Cases with Context and Evaluation for Efficient Software Development (SUCCEED) system to support the implementation of new upcycling initiatives by effectively sharing cases within the organization. To ascertain the efficacy of upcycling within our proposed model and system, we formulated three research questions and conducted two distinct experiments. Through surveys, we identified motivations and characteristics of shared upcycling-relevant development cases. Development tasks were divided into groups, those that employed the SUCCEED system and those that did not, in order to discern the enhancements brought about by upcycling. As a result of this research, we accomplished a comprehensive structuring of both technical and experiential knowledge beneficial for development, a feat previously unrealizable through conventional software reuse, and successfully realized reuse in a proactive and closed environment through construction of the wisdom of crowds for upcycling cases. Consequently, it becomes possible to systematically perform software upcycling by leveraging knowledge from existing projects for streamlining of software development. [ABSTRACT FROM AUTHOR]

This paper proposes a knowledge-based system to help parents diagnose some of their children's diseases at an early stage and to give them insight into whether they should see a doctor. It covers the most common diseases encountered by pediatric specialists, such as intestinal obstruction, dehydration, anemia, etc. An overview of these diseases is provided, the etiology of the disease is outlined, and treatment suggestions for the most common diseases are given when possible. CLIPS was used to develop the proposed expert system. [ABSTRACT FROM AUTHOR]

Abstract Precise cost estimation is the first and crucial step for decision-makers at the planning stage of product development. Due to the unavailability of knowledge and the complex nature of manufacturing processes, materials, and product designs, the selection of suitable alternatives based on estimated cost has become more challenging. The purpose of this paper is to present the development of a knowledge-based system (KBS) for assisting decision-makers in identifying suitable alternatives for precise cost estimation in a mistake-proof environment. The system architecture is comprised of three layers namely the database layer, application layer, and end-user layer, and developed using knowledge-based engineering, set-based concurrent engineering, and poke-yoke as lean enabling approaches. Based on the captured knowledge through industrial investigation and experimentation, a real-time system application was developed using C# language and validated using a real case study in the foundry industry using sand casting as the manufacturing process. It ensured the validity of the developed system. KBS provides an extensive tool to decision-makers for accurate decision-making at the design stage using cost and other associated values. Further, it facilitates the practitioners of every business sector to capture, save, and use the identified knowledge for problem-solving in the future.

Selecting the appropriate temporary facilities is important for reducing cost and improving the productivity and safety of craft professionals in construction projects. However, the manual planning process for scaffolding systems is typically prone to inefficiencies. This paper aims to develop a knowledge-based framework for a scaffolding decision support system for industry. An integrated two-phase system was established, including a technical evaluation module and a knowledge-based module. First, the system identifies feasible scaffolding alternatives from the database through a rule-based algorithm. Second, a knowledge-based module was designed to assess the alternative performance. The framework effectively generated the ranking of scaffolding alternatives, and the top three influential factors were identified, including the site accessibility, protection to workers and health risk. Thus, an application study of an industrial steel project was proffered to validate the effectiveness of the framework. The proposed framework may help decision-making regarding the implementation of temporary facility planning in industry practices. It has wider applicability because it simultaneously considers site conditions, productivity, safety, and financial benefits, and is designed and implemented through a computerized path. The paper contributes to the industry by developing an integrated decision support system for temporary facilities. Additionally, the practical contribution of this research is the provision of an optimized scaffolding planning method that could be utilized as a guide when implementing the decision support system. [ABSTRACT FROM AUTHOR]

In this paper, the operation process of cleaning of intelligent rice–wheat combine harvester is divided into two key steps: initial setting of cleaning device operation parameters and dynamic control of cleaning device operation parameters. Combined with the operation experience of cleaning control of agricultural machinery operators, the dynamic control knowledge-based system of cleaning device operation parameters was built based on production rule reasoning. The cleaning device of the rice–wheat combine harvester is intelligently controlled based on the dynamic monitoring and control system of the cleaning device operation quality and operation parameters, so as to achieve the purpose of controlling the cleaning operation quality of the rice–wheat combine harvester in the normal range. Through the field experiment results and analysis, it is proved that the intelligent control system of the cleaning device operation parameters based on the dynamic control knowledge-based system of cleaning device operation parameters can effectively keep the cleaning impurity content and loss rate of intelligent rice–wheat combine harvester in the normal range, so as to verify the effectiveness of the intelligent control knowledge-based system of the cleaning device operation parameters. [ABSTRACT FROM AUTHOR]

In this paper, we analyze selected structural and dynamic properties of uninorm Petri nets in terms of their use in modeling knowledge-based systems operating in an uncertain environment. The uninorm Petri nets use uninorms to describe their behavior. Uninorms are a special type of aggregation functions that generalize both t-norms and s-norms. This generalization is that in the case of a uninorm, the neutral element can take any value in the unit interval, in contrast to triangular norms, for which the neutral element is equal to 1 or 0 depending on whether the triangular norm is a t-norm or an s-norm. Thanks to this, by manipulating the neutral element in the unit interval, it is possible to better match the behavior of the used net to the requirements of the modeled system. When modeling systems using uninorm Petri nets, as well as nets using triangular norms, the following questions are important for the designer of such models: (1) What properties of the net affect the uniqueness of the model's decisions? (2) How to choose uninorms assigned to net transitions so that the behavior of the net model is deterministic? This work provides answers to these two questions above. The subject of this paper is a continuation of the author's earlier research on generalized fuzzy Petri nets based on triangular norms. This also falls within the range of challenges outlined in a review article on the state of research on the theory and applications of fuzzy Petri nets, developed by Zhou & Zain authors and published in Artificial Intelligence Review, 45, 405–446, 2016. [ABSTRACT FROM AUTHOR]

The process of generating, disseminating, using, and managing an organization's information and knowledge is known as knowledge management (KM). Conventional KM has undergone modifications throughout the years, but documentation has always been its foundation. However, the significant move to remote and hybrid working has highlighted the shortcomings in current procedures. These gaps will be filled by artificial intelligence (AI), which will also alter how KM is transformed and knowledge is handled. This article analyzes studies from 2012 to 2022 that examined AI and KM, with a particular emphasis on how AI may support businesses in their attempts to successfully manage knowledge and information. This critical review examines the current approaches in light of the literature that is currently accessible on AI and KM, focusing on articles that address practical applications and the research background. Furthermore, this review provides insight into potential future study directions and improvements by presenting a critical evaluation. [ABSTRACT FROM AUTHOR]

Semantic checking of railway infrastructure information support data is one of the ways to improve the consistency of information system data and, as a result, increase the safety of train traffic. Existing ontological developments have demonstrated the applicability of description logic for modelling railway transport, but have not paid enough attention to the data resources structure and the railway regulatory support. In this work, the formalization of the tabular presentation of data and the rules of railway transport regulations is carried out using the example of a connection track passport and temporary speed restrictions using ontological means, data wrangling and extraction tools. Ontologies of the various formats data resources and railway station infrastructure, tools for converting and extracting data have been developed. The semantic checking of the compliance of railway information system data with regulatory documents in terms of the connection track passport is carried out on the basis of a multi-level concretization model and integration of ontologies. The mechanisms for implementing the constituent ontologies and their integration are demonstrated by an example. Further research includes ontological checking of natural language normative documents of railway transport.

Additive Manufacturing (AM) is becoming data-intensive. The ability to identify Data Analytics (DA) opportunities for effective use of AM data becomes a critical factor in the success of AM. To successfully identify high-potential DA opportunities in AM requires a set of distinctive interdisciplinary knowledge. This paper proposes a methodology that enables collaborative knowledge management for identifying and prioritizing DA opportunities in AM. The framework of the proposed methodology has three components: a team of experts, a DA Opportunity Knowledge Base (DOKB), and a prioritization tool. The team of experts provides diverse knowledge that can be used to identify and prioritize DA opportunities. The DOKB, developed by using the Web Ontology Language (OWL), captures diverse knowledge from the experts to identify DA opportunities. The prioritization tool ranks the identified DA opportunities by using the Fuzzy integrated Technique of Order Preference Similarity to the Ideal Solution (Fuzzy-TOPSIS). A case study, in which National Institute of Standards and Technology (NIST) researchers participated, demonstrates our methodology. As a result, 264 DA opportunities for AM's Laser-Powder Bed Fusion (L-PBF) process are identified and prioritized. The prioritized DA opportunities help set a DA direction for L-PBF AM. Our methodology keeps knowledge sharable, reusable, revisable, and extendable. Thus, this methodology can continue to facilitate collaboration within the AM community to identify high potential and high impact DA opportunities in AM. [ABSTRACT FROM AUTHOR]

An infant in the first 28 days following birth is referred to as a newborn baby. In Ethiopia, neonatal mortality is a serious problem that accounts for the lion's share of under-five mortality. Diagnosis and treatment of infant disease need specialized medical resources with plenty of expert knowledge and experience. Globally and particularly in low-income countries, there is a lack of such professional which make the diagnosis and treatment more difficult. The goal of this paper is to design a knowledge-based system for the diagnosis and treatment recommendation of neonatal diseases by collaborating with the knowledge obtained from machine learning and health experts. Design science research approach has been employed as the overall research design, and the hybrid data mining process model is used to extract knowledge from the collected clinical dataset. To this end, three classification algorithms in WEKA tools, namely, J48, PART, and JRip, were considered. Then, a partial decision tree (PART) algorithm under 10-fold cross-validation achieved the highest performance result with an accuracy of 98.06% and the researchers decided to use the generated rules for the development of a knowledge-based system. Evaluation results show that the developed prototype registers 90.9% accuracy in system performance testing and 89.2% in user acceptance testing. In conclusion, the system is used as an assistant tool for healthcare experts and could be effective if it could be implemented. [ABSTRACT FROM AUTHOR]

The drive by the government of Tanzania to electrify every village has resulted into expansion of the electrical secondary distribution networks (ESDNs). Therefore, maintenance management is of the highest priority for the smooth operation of the ESDNs to reduce unscheduled downtime and unexpected mechanical failures. Studies show that condition-based predictive maintenance (CBPdM) method allows the utility company to monitor, analyze and process the information obtained from ESDNs transformers. Thus, this study adopts the CBPdM method to develop a maintenance scheduling algorithm that can predict the transformer state, forecast maintenance time based on transformer load profile and schedule its maintenance using a knowledge-based system (KBS). Applying the challenge driven education approach, the requirements for developing an algorithm were established through an extensive literature survey and engagement of the key stakeholders from the Tanzania utility company. Our study uses the Dissolved Gas Analysis tool to collect the transformer parameters used in algorithm design. The parameter analysis was performed using Statistical Package for Social Sciences software. Results show that the designed KBS algorithm minimizes human-related maintenance errors and lowers labour costs as the system makes all the maintenance decisions. Specifically, the proposed maintenance scheduling algorithm reduces downtime maintenance costs by 1.45 times relative to the classical inspection-based maintenance model while significantly saving the maintenance costs. [ABSTRACT FROM AUTHOR]

In this paper, an effort has been made for intense review on Knowledge-Based Expert System (KB-ES) applications in manufacturing planning. Uniqueness of the present review work is addressed in terms of analysis on published review articles and their review gap. Research works exemplified between 1981 and 2016 were reviewed in terms of ES application in handling product variety, execution of process planning activities, machining, tool selection, tool design, welding, advanced manufacturing, product development. A statistical analysis was carried out in relation with number of publications, domain-specific area and their percentage contribution. It was inferred that, most of the work focused on ES applications related to tool design and machining apart from execution of various process planning activities. Future research can focus on the development frame-based, object oriented-based, ontology-based knowledge representation in order to develop robust system in decision-making for handling complex engineering problem. ES applications can be extended to field of micro fabrication, machine tool development and integrated system development from design to manufacturing. [ABSTRACT FROM AUTHOR]

Illegal transshipment of maritime ships is usually closely related to illegal activities such as smuggling, human trafficking, piracy plunder, and illegal fishing. Intelligent identification of illegal transshipment has become an important technical means to ensure the safety of maritime transport. However, due to different geographical environments, legal policies and regulatory requirements in each sea area, there are differences in the movement characteristics and geographical distribution of illegal transshipment behavior in different time and space. Moreover, in areas with dense traffic flow, normal navigation behavior can easily be identified as illegal transshipment, resulting in a high rate of misidentification. This paper proposes a hybrid rule-based and data-driven approach to solve the problem of missing identification in fixed threshold methods and introduces a traffic density feature to reduce the misidentification rate in dense traffic areas. The method is both interpretable and adaptable through unsupervised clustering to get suitable threshold distribution combination for regulatory sea areas. The evaluation results in two different sea areas show that the proposed method is applicable. Compared with other widely used identification methods, this method identifies more illegal transshipment events, which are highly suspicious, and gives warning much earlier. The proposed method can even filter out misidentification events from compared methods' results, which account for more than half of the total number. [ABSTRACT FROM AUTHOR]