Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modelling capability as well as a high-level of customisation and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad-hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such a complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as it suitable within a number of applications contexts - i.e. as a research/educational tool, communication framework, gamification and participatory modelling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features.

Over the past decade, Knowledge bases (KB) have been increasingly utilized to complete and enrich the representation of queries and documents in order to improve the document retrieval task. Although many approaches have used KB for such purpose, understanding how effectively lev-erage entity-based representation still needs to be resolved. This paper proposes a Purely Enti-ty-based Semantic Search Approach for Information Retrieval (PESS4IR) as a novel solution. The approach includes (i) its own entity linking method, (ii) an inverted indexing method, and for document retrieval and ranking, (iii) an appropriate ranking method is designed to take advantage of all the strengths of the approach. We report the findings on the performance of our approach tested by queries annotated by two known entity linking tools, REL and DBpedia-Spotlight. The experiments are performed on the standard TREC 2004 Robust and MSMARCO collections. By using the REL method, for queries whose all terms are annotated and whose average annotation scores are greater than or equal to 0.75, our approach achieves the maximum nDCG@5 score (1.000). Thus, using our approach with any other document retrieval method would be an added value, unless that method achieves the maximum nDCG@5 score for those highly annotated queries.

Saudi Arabia is an oil-reliant nation as a large percentage of its GDP comes from oil resources. Oil dependency leaves a county at the mercy of the international crude market, and a decrease in the price of crude can seriously destabilize the economy of such nations. An example is the case of Venezuela whose dependence on oil caused a national disaster (McCarthy, 2017). As such, the nation’s exports, GDP, and government revenue are primarily dependent on oil revenue, and the recent decrease in the oil prices has decreased Venezuela’s national revenue resulting in economic collapse as well as inflation. A shift from a resource based economy to a knowledge based economy will help Saudi Arabia become less reliant on its oil revenues for its economic stability and growth (Nurunnabi, 2017).

In this digital world, organisations are facing global competition as well as manpower pressures leading towards knowledge economy, which heavily impacts on their local and international businesses. The trend is to foster collaboration and knowledge sharing to cope with these problems. With the advancement of technologies and social engineering that can connect people in virtual world across time and distance, several organisations are embarking on knowledge management (KM) systems, implementing community of practice (CoP) approach. However, virtual communities are relatively new paradigms, and there are several challenges to their successful implementation from an organisation’s point of interest. There is lack of CoP implementation framework that can cater to today’s dynamic business and sustainability requirements. To fill the gap in literature, this paper develops a practical framework for a CoP implementation with a view to align KM strategy with business strategy of an organization. It explores the different steps of building, sharing and using tacit and explicit knowledge in CoPs by applying Wiig KM cycle. It proposes a practical CoP implementation framework that adopts the Benefits-Tools-Organisation-People-Process (BTOPP) model in addressing the key questions surrounding each of the BTOPP elements with a structured approach. Finally, it identifies key challenges such as organizational culture and performance measurements, and provides practical recommendations to overcome them for a successful CoP implementation.

The process of Targeted Opinion Word Extraction (TOWE), a critical component of aspect-based sentiment analysis (ABSA), revolves around identifying opinionated words linked to specific aspect-terms within sentences. Existing deep learning approaches, while effective, often overlook the syntactic structure of sentences, a factor that previous studies have identified as beneficial for TOWE. In this study, we introduce the Syntactic-Enhanced Deep Learning Model (SEDLM) that integrates syntactic structures into deep learning frameworks for TOWE. Our approach leverages syntax-driven opinion potential scores and syntactic inter-word connections, enhancing model performance. Additionally, we introduce an innovative regularization strategy aimed at distinguishing word representations in TOWE tasks. Our comprehensive analysis reveals that SEDLM sets new benchmarks in performance across multiple standard datasets.

For disciplines like biological science, security, and the medical field, link prediction is a popular research area. To demonstrate the link prediction many methods have been proposed. Some of them that have been demonstrated through this review paper are TransE, Complex, DistMult, and DensE models. Each model defines link prediction with different perceptions. We argue that the practical performance potential of these methods, having similar parameter values, using the fine-tuning technique to evaluate their reliability and reproducibility of results. We describe those methods and experiments; provide theoretical proofs and experimental examples, demonstrating how current link prediction methods work in such settings. We use the standard evaluation metrics for testing the model's ability.

China promotes nature-based solutions (NbS) as key approaches to addressing climate change, ecosystem damage, and biodiversity loss. However, indigenous and local knowledge (ILK), which is recognized by international scholars as an essential element of successful NbS, has not been thoroughly studied in the Chinese context. By conducting a comprehensive review of Chinese central governmental policies and semi-structured interviews with typical cases, this study finds out diversified sources of ILK and five pathways of ILK integration, including education, supervision, participation, knowledge preservation, and knowledge adoption. An increasing emphasis on community engagement has been driven by central policies such as the ecological civilization, carbon peaking and carbon neutrality goals. From a practical perspective, consistent core ideas of ILK exist in selected typical Chinese cases and studies in other countries, namely co-existence and reciprocity with nature. While supportive central policies can set the scene, ILK integration could not be realized without local governmental endorsement, context-based implementation, and long-term multi-stakeholder participation. Furthermore, this study also figures out a potential deficiency in terms of realizing effective ILK integration in mainstream and conventional practices, which can be further investigated by future studies.

The rapid evolution of technology has reshaped the global economy, intensifying competition and prompting industry players to embrace innovation for sustained competitiveness. In Indonesia, Business Incubators, overseen by Universities and Provincial/City Governments, are pivotal. However, understanding the determinants of Business Incubator Performance remains limited. This study investigates 24 statement items aiming to uncover the factors influencing Technology Business Incubators in Indonesia, with a focus on knowledge management as a mediating variable derived from a knowledge-based perspective. Employing a quantitative approach, Structural Equation Modeling (SEM) is utilized to scrutinize these factors' impact on Business Incubator Performance. The findings reveal that knowledge management fully and partially mediates between funding support, government assistance, incubator governance, and Business Incubator Technology Performance. This research offers valuable insights for entrepreneurs and stakeholders, emphasizing the significance of funding, governmental backing, incubator governance, and knowledge management in enhancing Incubator Business Technology Performance in Indonesia.

The innovation driven Industry 5.0, in agreement with Industry 4.0, leads to consider human in a prominence position as the center of manufacturing field. This pushes towards the hybridization of manufacturing plants promoting a fully collaboration between human and robot. Furthermore, the new paradigm of "human centred design" and "anthropocentric design" allows enabling a synergistic combination of human and robot skills. However, properly collaborative workplaces are currently very few. Industry is still not confident, and systems integrators hesitate to venture into Human-Robot Collaboration (HRC). Despite the effort in collaborative robotics, a general solution to overcome the current limitations in designing of collaborative workplaces still misses. In the current work, a Knowledge-Based Approach (KBA) is adopted to face collaborative workplace designing problem. The framework resulting from the KBA allows developing a modelling paradigm that enable to define a streamlined approach for the layout designing of a collaborative workplace. Finally, a what-if analysis and a ANOVA analysis are performed to generate and evaluate a set of scenarios related to a collaborative workplace for quality inspection of welded parts. Facing the high complexity and multidisciplinary of HRC can be conveyed to develop a general design approach aimed at overcoming the difficulties that limit the spread of HRC in the manufacturing field.

Exploiting the linguistic knowledge of the source language for neural machine translation (NMT) has recently achieved impressive performance on many large-scale language pairs. However, since the Turkish→English machine translation task is low-resource and the source-side Turkish is morphologically-rich, there are limited resources of bilingual corpora and linguistic information available to further improve the NMT performance. Focusing on the above issues, we propose a multi-source NMT approach that models the word feature in parallel to external linguistic features by using two separate encoders to explicitly incorporate linguistic knowledge into the NMT model. We extend the word embedding layer of the knowledge-based encoder to accommodate for each word’s linguistic annotations in the context. Moreover, we share all parameters across encoders to enhance the representation ability of the NMT model on the source language. Experimental results show that our proposed approach achieves substantial improvements of up to 2.4 and 1.1 BLEU scores in Turkish→English and English→Turkish machine translation tasks, respectively, which points to a promising way to utilize the source-side linguistic knowledge for the low-resource NMT.

Nurses’ competency towards Evidence-based Practice (EBP) has been extensively investigated by several studies worldwide. However, factors affecting the competence of Greek nurses working in the NHS have not been fully investigated in terms of EBP. Thus, this study aimed to explore the impact of individual qualifications of nurses on their competence towards EBP. Data from 473 registered nurses working in 10 hospitals of in the Greek National Health Service (NHS) were collected between October and December 2022 using a convenience-sampling method in a cross-section design. The Greek version of the 35-item (5-point Likert scale) Evidence-Based Practice Competency Questionnaire for Professional Registered Nurses (EBP-COQ Prof) was used to assess the competence level of nurses focusing on the attitudes, skills, and knowledge as well as the utilization of EBP in clinical practice. One-way ANOVA and Pearson coefficient tests were applied to compare possible differences among variables (two or more groups) as appropriate. A multi-factorial regression model was applied to explore participants’ qualifications, including demographics (MSc degree, gender, English language knowledge, etc.) as independent variables, and to control for potential confounding effects toward EBP competency. P-values <0.05 were considered statistically significant. The mean age of 473 participants (402 women and 71 men) was 44.7 ± 9.2 years old. The mean value of competence subscales was found as follows: attitudes 3.9±0.6, knowledge 3.7±0.6, skills 3.1±0.8, and utilization 3.4±0.7. A multivariate regression analysis revealed that associates of “Master's degree” (t=3.039, p=0.003), “Writing an academic article” (3.409, p=0.001), “Working in a University clinic” (2.203, p=0.028) and, “Computer Skills” (2.404, p=0.017) are positively affecting ‘Attitudes’, ‘Knowledge’, ‘Skills’ and ‘Utilization’. Respectively. The research data suggest that nurses working in Greek NHS are limited in competence towards EBP in comparison to other European countries. Therefore, Vocational, Educational and Training programs tailored to EBP enhancement are crucially important.

Due to their increased surface area, extent of swelling and active substance loading capacity and flexibility, nanogels made from natural and synthetic polymers have gained significant interest in the scientific and industrial areas. Especially, customized design and implementation of non-toxic, biocompatible, and biodegradable micro/nano carriers makes their usage very feasible for a range of biomedical applications, including drug delivery, tissue engineering, and bioimaging. The design and application methodologies of nanogels have been outlined in this review. Additionally, the most recent advancements in nanogel biomedical applications have been discussed, with a particular emphasis on applications for the delivery of drugs and biomolecules.

The aim of this paper is to contribute to the body of knowledge about Project-Based Learning (PBL) methodology in higher education by describing and analysing interrelations between competencies, and their contribution to knowledge transfer (KT) and students’ rating of the project. The sample consisted of 464 students from the Universities of Huelva (N=347; 74.8%) and Murcia (N= 117; 25.2%), enrolled in the second year of a degree in either Infant or Primary Education. Data was collected through a self-administered questionnaire comprising a total of 53 items measuring General, Specific and Transversal competencies, as well as students’ rating of the project. Competencies were selected from the course programmes for the degrees in Infant and Primary Education. Preliminary results showed that competencies were moderately to highly acquired after PBL, and that students reported notable KT as well as a positive assessment of the project. KT showed a high degree of association with students’ ratings and was established as a key factor in learning and learner satisfaction in higher education.

In this paper I take the book by Michel Serres “Thumbelina” as the occasion for a reflection on the conceptual basis of knowledge management, as it was built by Ikujiro Noanka and co-workers. The direct access to knowledge that Thumbelina practices together with her peers is, in fact, for me, a god observation point to bring the reflection of Nonaka further, towards the discovery of a new understanding of knowledge and knowing processes. If the digital revolution is third step after writing and printing, in the soft changes in the relations between human beings and knowledge, then it poses in an urgent manner the problem to deepen our understanding of what knowledge and intelligence are and to change our practice at the education level and to design new digital tools to support our knowledge management processes.

Diabetes management requires constant monitoring and individualized adjustments. This study proposes a novel approach that leverages digital twins and Personal Health Knowledge Graphs (PHKGs) to revolutionize diabetes care. Our key contribution lies in developing a real-time, patient-centric digital twin framework built on PHKGs. This framework integrates data from diverse sources, adhering to HL7 standards and enabling seamless information access and exchange. PHKGs offer a flexible and efficient format that supports various applications. As new knowledge about the patient becomes available, the PHKG can be easily extended to incorporate it. This dynamic approach fosters continuous improvement and facilitates the development of new applications. As proof of concept, we have demonstrated the versatility of our digital twin by applying it to different use cases in diabetes management. These include predicting glucose levels, optimizing insulin dosage, providing personalized lifestyle recommendations, and visualizing health data. By enabling real-time, patient-specific care, this research paves the way for more precise and personalized healthcare interventions, potentially improving long-term diabetes management outcomes.

Financial markets require a great deal of decision making from the investors and market makers. One metric that can help ease the process of decision making is investment risk which can be measured in two parts; systematic risk and idiosyncratic risk. Clear understanding of the volatilities in each risk component can be a powerful signal in recognizing the right assets to maximize the investment returns. In this paper, we focus on the idiosyncratic volatility values and pre-calculate the idiosyncratic volatility values for 31,198 members of NYSE, Amex and Nasdaq markets for the trades occurring between January 1963 and December 2019. Utilizing a subset of dataset, limited to Nasdaq100 index, we consider the application of machine learning techniques in predicting the idiosyncratic volatility values using the raw trade data to explore a data extension option for the future market trade records that have not yet occurred. We offer a deep learning based regression model and compare it with traditional tree-based methods on a small subset of our per-calculated idiosyncratic volatility dataset. Our analytical results show that the performance of the deep learning techniques is much more robust in comparison to that of the traditional tree-based baselines.

In his Cynefin classification of system behaviours, Snowden (2007), posits that his Simple (Clear ) and even Complicated system categories are sufficiently understandable to be amenable to the traditional methods of system analysis and modelling. Here, relationships between entities in the system are clear and unambiguous and they behave predictably. So, we can build mathematical “models” to describe how the system is expected to function in different situations. Leaving aside his fourth category, Chaotic, as too hard for the moment, this paper focusses on the third category, Complex systems. Yaneer Bar-Yam [1998] defined "complex systems" as systems that "have multiple interacting components, whose collective behaviour cannot be simply inferred from the behaviour of components." So, the traditional system modelling approach of decomposition into components and attempting to build up a picture of system performance from individual pieces, alone, is no longer appropriate for this class. The problem is that most of the methods we currently employ, and particularly the methods that attempt to make the case that these systems are safe, (such as Fault trees, FMEA. and Root Cause Analysis), rely on this decomposition into components and attempt to make the parts more reliable individually. Even the more systemic approaches, such as Reason’s (1990) Swiss Cheese Model, and Leveson’s System Theoretic Analysis (STPA) (2004), encourage adding layers of protection, barriers, strengthened control loops and better safety critical systems, in the hope that they are making the systems safer. But without a valid model to test the effectiveness of these add-ons, we cannot be sure that they will not have an opposite effect, of making the systems even more complex, unreliable and unpredictable. For complex systems, the careful gathering and documentation of system components and their relationships is still a necessary first step, but we need an additional capacity to model these complex systems to test out and prove they work safely and successfully in the real world. (not just draw fixed component relationships as system “models” (wiring diagrams)) In the last 10 years, an original approach developed by Hollnagel, (2012), to model systems as sets of interactive, interdependent “functions”, (abstracted from and independent of component details), has now been developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, such as MBSE( Goldberg (1994)), and can pull it all together in a dynamic system model from which analysts can discern how it really works in practice, and predict the emergent behaviours characteristic of complex systems. The paper describes how this methodology developed and how it is currently used to model real situations, to analyse incidents and anticipate events.

The deployment of Artificial Intelligence (AI) systems in decentralized environments is on the rise, yet representation of shared conceptualization in such scenarios remains a challenging issue. The absence of a shared understanding can lead to suboptimal performance of AI systems and hinders the ability to comprehend the knowledge and beliefs of agents in the domain. This paper proposes a formal model for modeling conceptualization in AI systems that integrates ontology, epistemology, and epistemic logic. The model aims to address the gap in representing shared conceptualization in decentralized environments and enhance the performance of AI systems operating in such environments. The proposed model is a hybrid structure that blends extensional and intensional structures and leverages logic-based languages for modeling purposes. A case study in the healthcare sector is presented to illustrate the application of the proposed model. The study contributes to the existing literature by providing a formal model for representing shared conceptualization in decentralized environments, which can be utilized to optimize the performance of AI systems in these environments.

With the increasing use of automatic transmissions in passenger vehicles, there is a growing need for improved gear shifting control strategies that aim to enhance performance and fuel efficiency. This paper introduces a knowledge-based fuzzy control system designed specifically for automatic transmissions in light vehicles. The proposed system utilizes a fuzzy inference system as the controller, enabling the emulation of the driver’s perceptions during the gear shifting process. Consequently, the input signal consists of variables that are either sensitive to or observable by the driver, while the output represents the selected gear. The paper evaluates three distinct control strategies, each based on different inputs. These inputs include the longitudinal velocity of the vehicle, the angular velocity of the engine, or a combination of both, along with the time-derivative of the throttle valve opening. Through the evaluation, it is observed that the combination of inputs yields significantly improved performance. Therefore, the methodology presented in this paper demonstrates that the combined input approach is more effective for gear shifting control.

The future of machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved in labs for the most common machining operations by using models based on equations describing the physical laws of the machining processes; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep-knowledge and models” that aid machine designers, production engineers or machinists to get the best of the machine-tools. This article proposes a methodology to reduce problems in machining by means of a simulation utility, which uses the main variables of the system&process as input data, and generates results that help in the proper decision-making and machining planification. Direct benefits can be found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter-free optimum cutting conditions and d) the right programming of cutting toolpaths at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies and are explained in the paper.

The next future using machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved for the most common cases by sing models based on equations describing the physical laws dominating the machining process; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep knowledge and models” that aid the machine designer, the production engineer, or machinists to get the best of their machines. This article proposes a systematic methodology to reduce problems in machining by means of a simulation utility, which recognizes, collects and uses the main variables of the system/process as input data, and generates objective results that help in the proper decision-making. Direct benefits by such an application are found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter free optimum cutting conditions and the right programming of cutting tool path at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies.

This paper presents how social media platforms and their components could be integrated into the KMS for the purpose of managing and sustaining Somali Indigenous knowledge (IK). The discovery of knowledge and its utilization is now fuelled by the advancement of technology, which connects users to external sources through using different platforms. The rapidly increasing use of SM and mobile technologies create opportunities to form knowledge networks that can facilitate the process of creating, preserving, and sharing knowledge and skills that are unique to communities in the Somali context. Somali society being an oral society with rich IK and other cultural practices lacked a framework in place to organize the existing knowledge in various methods. The paper refers to existing methodology E-learning and KM functions. This method first identifies functional similarities between SM, E-Learning, and KM systems and their interactions if they are properly integrated. The proposed framework consists of two functional parts; KM and SM. The KM part involves knowledge capturing, validation, formatting, storing, and distribution. SM part of the framework involves knowledge presentation, sharing, and application. Since the Somalis are said to be an oral society, this framework helps to capture and disseminate IK residing in knowledge holders.

List-only entity linking is the task of mapping ambiguous mentions in texts to target entities in a group of entity lists. Different from traditional entity linking task, which leverages rich semantic relatedness in knowledge bases to improve linking accuracy, list-only entity linking can merely take advantage of co-occurrences information in entity lists. State-of-the-art work utilizes co-occurrences information to enrich entity descriptions, which are further used to calculate local compatibility between mentions and entities to determine results. Nonetheless, entity coherence is also deemed to play an important part in entity linking, which is yet currently neglected. In this work, in addition to local compatibility, we take into account global coherence among entities. Specifically, we propose to harness co-occurrences in entity lists for mining both explicit and implicit entity relations. The relations are then integrated into an entity graph, on which Personalized PageRank is incorporated to compute entity coherence. The final results are derived by combining local mention-entity similarity and global entity coherence. The experimental studies validate the superiority of our method. Our proposal not only improves the performance of list-only entity linking, but also opens up the bridge between list-only entity linking and conventional entity linking solutions.

Model-based approaches form the basis of the majority of damage detection and localization studies; however, built-in online detection technique can offer an alternative for future structural health monitoring technologies. Thus, in this paper, we present a dynamic localization algorithm for damage localization in functionally graded plates. The method depends on the creation of a grid matrix that contains the dynamic response of the structure over time. Subsequently, an optimization process is carried out via a linear equation representing the information contained within the grid to achieve accurate localization of damage. Due to the inherent sparsity of the localization nature, the FISTA algorithm has been employed as a problem solver. The approach is tested in the case of the functionally graded plate under clamped free boundary conditions. Several, damage scenarios were investigated for the case of damage signals on and off the grid. The results show that the proposed approach is capable of accurately predicting damage position and can be suitable for use in low size data systems.

This thesis presents the effects of work done on a software project for generative models and spreadsheets, allowing for a quick creation of the conceptual model of the aircraft. The subject of the work is a response to the current trends and needs prevailing in the field of computer design engineering CAD and aviation. In the initial chapters, theoretical issues related to the work being carried out were introduced and the methodology of creating software for construction and verification of the structure of aircraft along with the needs of interchange between databases of generative models was presented. In the next stages, the concepts and selected solutions for the user interface supporting the knowledge base were presented along with a set of procedures for its operation. Furthermore, the method of database integration with the methods of determining design features for the developed generative models and with the Siemens NX system. Furthermore, problems encountered in software development, as well as solution examples for model application are specified. The results obtained and the models generated on their basis were subjected to a strength analysis using Autodesk Inventor software and analysed in terms of meeting the initial assumptions. In the end, conclusions and observations resulting from the effects of the work presented in the project were formulated.

A new research stream emerged in the 2000s dedicated to flexible work arrangements in public and private organizations, called “new ways of working” (NWW). This article aims to examine NWW from both a theoretical and empirical perspective, focusing on outcomes of this new concept and the debate between “mutual gains” vs. “conflicting outcomes.” Through a literature review, it examines this research field’s innovation and its rather vague theoretical foundations. Findings demonstrate that NWW definitions are diverse and somewhat imprecise, leading to fragmented research designs and findings; the research stream’s theoretical foundations should be better addressed. Findings also highlight the current lack of empirical data, which therefore does not allow any real conclusions on NWW’s effects on employees’ and organizations’ well-being and performance.

Previously, we described the initial use of Flipped Team‐Based learning (FTBL) defined as TBL approach combined with flipped classroom learning methodology, in which students previewed online lectures and applied their knowledge in different in-class activities. The purpose of the present study is to review the progress within this approach and to investigate how constant changes in team allocation can affect student’s perception regarding this modified FTBL approach. Although students showed reluctance initially to get out of their ‘comfort zone’, our findings show that learners perceived the adoption of the continued random allocation, and became accustomed to this learning approach, which finally assisted them to enhance their team-work skills and classroom performance, to develop their reflective capabilities as well as improving their rapport building skills, learning and academic performance. Learners also believed that this learning strategy that creates critical incidents can simulate their future work environment as they might be expected to work in unfamiliar situations. Therefore, the present study indicated strong support for the modified FTBL method and was seen to work exceptionally well, despite some minor problems that students can experience working in a team and/or with different teammates in every session.

.Most of the accident cases at work are caused by unsafe behaviour. Scholars have thus extensively researched factors of safe behavior.Safety leadership is stated as the most effective factor in safety behaviour. Besides, safety knowledge and safety attitudes are also found to influence safety behaviour. The purpose of this paper is to investigate the nature of the relationship between safety leadership, safety knowledge, safety attitude, and safety behavior, which has been found to be limited or rare in previous research.A self-administered questionnaire was applied and was distributed randomly to 140 SME manufacturing workers and they were analysed using the structural equation modelling partial least squares (PLS-SEM). The results prove that safety leadership has a significant effect on safety knowledge, safety attitude, and safety behaviour, with safety knowledge and safety attitude mediating the safety leadership-safety behaviour relationship. Consequently, the results provide a more in-depth view of the relationship between safety leadership and safety knowledge-attitude-behaviour via adapting social-cognitive theory and social learning theory in solving safety issues in SME. This study also provides a starting point for researchers to better understand how effective safety leadership is provided by supervisors in terms of improving worker safety and safety knowledge-attitude-behaviour.

The complexity of modern power grids, exacerbated by integrating diverse energy sources, espe-cially inverter-based resources (IBRs), presents a significant challenge to grid operation and plan-ning since linear models fail to capture the intricate IBR dynamics. This study employs the Sparse Identification of Nonlinear Dynamics (SINDy) method to bridge the gap between theoretical un-derstanding and practical implementation in power system analysis. It introduces the novel Volterra-based Nonlinearity Index (VNI) to examine system-level nonlinearity comprehensively. The distinction of dynamics into first-order linearizable terms, second-order nonlinear dynamics, and third-order noise elucidates the intricacy of power systems. The findings demonstrate a fundamental shift in system dynamics as power sources transit to IBRs, revealing system-level nonlinearity compared to module-level nonlinearity in conventional syn-chronous generators. The VNI quantifies nonlinear-to-linear relationships, enriching our comprehension of power system behavior and offering a versatile tool for distinguishing between different nonlinearities and visualizing their distinct patterns through the proposed VIN profile.

Process mining offers significant services in the development of digital threads and digital twin applications for healthcare. One of the sub-emerging case studies is the use of patients’ location data in process mining analyses. While the streamlining of published works is focused on introducing process discovery algorithms, there is a necessity to address challenges beyond that. Literature analysis indicates that explainability, reasoning, and characterizing the root causes of process drifts in healthcare processes is an important but overlooked challenge. In addition, incorporating domain-specific knowledge into process discovery could be a significant contribution to process mining literature. To address this, we present the DIAG approach that consists of a meta-model and an algorithm that enables diagnosing in process mining through an ontology-driven approach. With DIAG, we modeled the healthcare semantics in a process mining application and diagnosed the causes of drifts in patients’ pathways. We performed an experiment in a hospital living lab to examine the effectiveness of the DIAG approach.

The capacitated lot-sizing problem with product recovery (CLSP-RM) holds significant importance in reverse logistics but is notoriously complex (NP-hard). In this study, two techniques are introduced to confront this challenge. The first technique entails devising a linear optimization task that eliminates capacity limitations across a wide problem spectrum, yielding a remarkably accurate approximation of the optimal solution. This adaptable approach presents a potent alternative and holds potential for extension to diverse problem categories owing to its versatile nature. The second technique employs a simulation methodology utilizing Halton’s uniform random numbers to address the issue. This randomized production search method sidesteps considerations of production costs, inventory expenditures, and production order when determining production batches. The research’s novelty lies in its application of these techniques to the problem. The suggested methods undergo evaluation via a benchmark dataset of approximately 4200 instances, with comparison against solutions derived through the Gurobi solver. The results underscore the efficacy and resilience of the introduced methodology in tackling the CLSP-RM predicament.

Abstract: New paradigms have emerged in mapping since the 20th century due to theoretical and practical advances in computer technology, communications, and cartography. For the design of the Expert-Topographic Map Service in Mexico, we adopted a theoretical framework of Cybercartography and expanded to incorporate expert systems and eventually other artificial intelligence resources. Using a transdisciplinary approach, our efforts could focus on the interconnection between knowledge and geotechnology to offer solutions to societal complex problems. This paper explores the theoretical pillars supporting the design of an Expert-Topographic Map Service, including cybercartography, knowledge-based systems and surface mathematical modeling. For the first time, cybercartography is applied in the realm of topographic mapping. As shown, a map conceived solely as an information system has been transformed, in the digital era, into a container of knowledge through the abstract representation of features like relief via the Triangular Irregular Network Model. Thereby, we can bridge from digital topographic maps to other knowledge-based models such as expert systems. The societal demand and empirical context for this research demonstrate that topographic maps and services are still at the core of cartographic initiatives in the 21st century.

COVID-19 has emerged as deadly pandemic worldwide with no vaccine or suitable antiviral drugs to prevent or cure the disease. Because of the time-consuming process to develop new vaccines or antiviral agents, there has been a growing interest in repurposing some existing drugs to combat SARS-CoV-2. Vitamin D is known to be protective against acute respiratory distress syndrome (ARDS), pneumonia and cytokine storm. Recently it has been used as a repurposed drug for the treatment of H5N1 virus-induced lung injury. Circumstantial evidences indicate that people with low level of vitamin D are more susceptible to SARS-CoV-2. Although, vitamin D was suggested to interfere with viral replication, its interaction with any SARS-CoV-2 protein is unexplored yet. Beside this, ivermectin, a well-known anti-parasitic agent, exhibits potent anti-viral activities in vitro against viruses such as HIV-1 and dengue. Very recently, ivermectin has been found to reduce viral load of SARS-CoV-2 in vitro. We have analyzed available structures of SARS-CoV-2 proteins to identify probable binding partner(s) of vitamin D and ivermectin through knowledge-based docking studies and figured out possible implication of their binding in SARS-CoV-2 infection. Our observations suggest that the non-structural protein nsp7 possesses a potential site to house 25-hydroxyvitamin D3 (VDY) or the active form of Vitamin D, calcitrol. Binding of vitamin D with nsp7 likely to hamper the formation of nsp7-nsp8 complex which is required to bind with RNA dependent RNA polymerase (RdRP), nsp12 for optimal function. On the other hand, potential binding site of ivermectin has been identified in the S2 subunit of trimeric spike(S) glycoprotein of SARS-CoV-2. We propose that deeply inserted mode of ivermectin binding at three inter-subunit junctions may restrict large scale conformational changes of S2 helices which is necessary for efficient fusion of viral and host membrane. Our study, therefore, opens up avenues for further investigations to consider vitamin D and ivermectin as potential drugs against SARS-CoV-2.

Sewage surveillance can be used as an effective complementary tool for detecting pathogens in local communities, providing insights into emerging threats and aiding in the monitoring of outbreaks. In this study using qPCR and whole genomic sewage surveillance, we detected Mpox virus along with other viruses, in municipal and hospital wastewaters in Belo Horizonte, Brazil over a 9 months period (from July 2022 till March 2023). MPXV DNA detection rates varied in our study, with 19.6% (11 out of 56 samples) detected through the hybrid capture method of whole genome sequencing and 20% (12 out of 60 samples) through qPCR. In hospital wastewaters, the detection rate was higher at 40% (12 out of 30 samples), compared to 13.3%(4 out of 30 samples) in municipal wastewaters. This variation could be attributed to the relatively low number of MPXV cases reported in the city, which ranged from 106 to 341 cases during the study period, and to dilution effects, given that each of the two wastewater treatment plants (WWTP) investigated serves approximately 1.1 million inhabitants. Additionally, nine other virus families were identified in both hospitals and municipal wastewaters including Adenoviridade, Astroviridae, Caliciviridae, Picornaviridade, Polyomaviridae, Coronaviridae (which includes SARS-CoV-2), Herspesviridae, Papillomaviridae and Flaviviridae (notably including Dengue). These findings underscore the potential of genomic sewage surveillance as a robust public health tool for monitoring a wide range of viruses circulating in both community and hospitals environments including MPXV.

Adaptation to the impacts of human induced global heating is inevitable. For a socially just and ecological sustainable adjustment to these new circumstances, we need to draw from diverse knowledge systems while, simultaneously, foster a social-ecological transformation. We approach these concerns through the concept of community-based adaptation (CBA) and discuss remedies. Synergies with feminist knowledge systems and indigenous ways of living show the transdisciplinary essence of CBA. By abstracting from its origin and overlapping disciplines, revealing the social metabolism as a common problem, CBA is prone to achieve more equitable and sustainable adjustments. Transdisciplinarity itself and the current related CBA literature point beyond mere adjustments and call for a redesign of the social metabolism, thereby shifting from adaptation to global heating towards adaptation with nature by focusing on social provisioning. An engagement with transformative adaptation and heterodox economics further shows the potential of CBA to serve as an instrument in support of a social-ecological transformation by addressing root causes of vulnerability and by fostering epistemic justice. Reflecting these points back on the epistemological and ontological assumptions of CBA, the viewpoint proposes critical realism as a guiding philosophy of science to move forward.

Complexity of issues in cancer research has led to the introduction of powerful computational tools for helping experimental in vivo and in vitro methods. These tools, typically focused on studying cell behavior and dynamic cell populations, range from systems of differential equations solved numerically, to lattice models and agent-based simulations. In particular, agent-based models (ABMs) are increasingly used for they capability to incorporate multi-scale features ranging from the individual up to the population level, thus combining statistically aggregated assumptions with individual heterogeneity. In this work, we present an ABM that simulates tumor progression in a colonic crypt, with the aim of providing an experimental in silico environment for testing results achieved in traditional laboratory research and developing alternative scenarios of tumor development. The model also allows some speculations about causal relationships in biologically inspired systems.

An accurate and timely precipitation forecast is essential for water resources management in hydropower, irrigation, and reservoir control. The conventional methods are limited by their inability to capture the high precipitation variability in time and space. In the present work, a wavelet-based deep learning approach is adopted to forecast precipitation using the lagged monthly rainfall, local climate variables, and global teleconnections such as IOD, PDO, NAO, and Nino 3.4 as predictors. The method was tested and validated over the Krishna River Basin in India. Overall, the forecasting accuracy was higher using the wavelet-based hybrid models than the single-scale models. The proposed multi-scale model was then applied to the different climatic regions of the country, and it was shown that the model could forecast the rainfall at reasonable accuracy for different climate zones of the country.

Purpose: Now traditional lecture-based teaching and learning have been affected by the COVID-19. The objectives of this article are to design the novel educational technique called ‘dynamic-based learning’ (DBL) that provides the combination of online teaching-learning methods and student’s creativity, to evaluate primary dynamic-based learning function, and to propose dynamic-based learning for higher education. Methods: DBL composes of four steps, including, preparation, homework, classroom, and evaluation, which was designed, and taught in medical and dental schools. Online support materials included mobile phone, email, Facebook Messenger, Line Messenger, Cisco Webex, and Zoom Meetings applications were recruited for this novel method. Results: A total of 32 third-year medical students and 26 sixth-year dental students was treated by DBL similarly. three subjects, including, Innovation in Dentistry, Basic Medical Research, and Principles of Pathology and Forensic Medicine were selected in this article. The results showed students could create their knowledge, ideas, and creativity during the online classes.Conclusion: DBL can be used as an alternative learning mode during the COVID-19 crisis. The benefits of DBL also include high flexibility, dynamic process, active learning, and high creativity. DBL should be tested with other disciplines such as engineering school, laws school, health sciences school, and should be compared with other traditional teaching and learning modes in the future. This method may support the global higher education systems to move forward the COVID-19 pandemic to set a novel standard of a future normal.

In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is of course exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology, (FRAM) which did allow the analyst in-sights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative ap-proach. In the last 10 years, this original approach developed to model systems as sets of interactive, interdependent “functions”, (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, and can pull it all together into dynamic models, (as opposed to static, fixed System Theoretic Process Accimaps), from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site.

Time and cost-efficient techniques are essential to avoid extra conventional experimental studies with large date-set to characterize the mechanical properties of composite materials. Correlation between the structural performance and mechanical properties could be captured through the efficient predictive models. Several ensembled Machine Learning (ML) methods were implemented in this study, to materially characterize carbon nanotube (CNT)-reinforced cement-based composites. Proposed models were compared with each other to represent the accuracy of each method. The Flexural and Compressive Strength (target values) of CNT reinforced composites were predicted based on the data-rich framework provided in previous experimental investigations. These data were utilized for training of the proposed models by employing SciKit-Learn library in Python, followed by hyper-parameter tuning and k-fold cross-validation method for obtaining an efficient model to predict the target values. Random Forest (RF) and Gradient Boosting Machine (GBM) were developed for this purpose. The findings of this study would be useful for prospective composite designers in case of sufficient experimental data availability for ML model training.

The popularity of electronic cigarettes in the United States and around the world has led to a startling rise in youth nicotine use. The Juul e-cigarette was introduced in the U.S. market in 2015 and had captured approximately 13% of the U.S. market by 2017. Unlike many other contemporary electronic cigarette companies, the founders behind the Juul® e-cigarette approached their product launch like a traditional high-tech start-up company, not like a tobacco company. This article presents a case study of Juul’s corporate and product development history in the context of US regulatory actions. The objective of this article is to demonstrate the value of government-curated archives as leading indicators which can (a) provide insight into emergent technologies and (b) inform emergent regulatory science research questions. A variety of sources were used to gather data about the Juul e-cigarette and the corporations that surround it. Sources included government agencies, published academic literature, non-profit organizations, corporate and retail websites, and the popular press. Data was disambiguated, authenticated, and categorized prior to being placed on a timeline of events. A timeline of four significant milestones, nineteen corporate filings and events, twelve US regulatory actions, sixty four patent applications, eighty seven trademark applications, twenty three design patents and thirty two utility patents related to Juul Labs and its associates is presented spanning the years 2004 thought 2020. This work demonstrates the probative value of findings from patent, trademark, and SEC filing literature in establishing a premise for emergent regulatory science research questions which may not yet be supported by traditional archival research literature. The methods presented here can be used to identify key aspects of emerging technologies before products actually enter the market, this shifting policy formulation and problem identification from a paradigm of being reactive in favor of becoming proactive. Such a proactive approach may permit anticipatory regulatory science research and ultimately shorten the elapsed time between market technology innovation and regulatory response.

Fractional cointegration in time series data has been explored by several authors, but panel data applications have been largely neglected. A previous study of ours discovered that the Chen and Hurvich fractional cointegration test for time series was fairly robust to a moderate degree of heterogeneity across sections, out of the six tests considered. Therefore, this paper advances a customized version of the Chen and Hurvich methodology to detect cointegrating connections in panels with unobserved fixed effects. Specifically, we develop a test statistic that accommodates variation in the long-term cointegrating vectors and fractional cointegration parameters across observational units. The behaviour of our proposed test is examined through extensive Monte Carlo experiments under various data generating processes and circumstances. The findings reveal that our modified test performs quite well comparatively and can successfully identify fractional cointegrating relationships in panels even in the presence of idiosyncratic disturbances unique to each cross-sectional unit. Furthermore, the proposed modified test procedure established the presence of long-run equilibrium between the exchange rate and labour wage of 36 countries agricultural markets.

This paper investigates the theoretical foundation for developing renewable geothermal resources locally. For this reason, we pay attention to the role of communities in geothermal development. This study presents arguments for a framework that approaches the geothermal resources as an endogenous factor of community development. To analyse it, we create a model that explains the local economic characters of the geothermal exploitation beyond its geological conditions. It aims to conceptualise a community based geothermal development standard referring to the endogeneity principle. Geothermal energy is given attention since the characteristics of this resource determine its use locally. We argue that the role of communities in geothermal exploitation is pivotal in the process of green growth for further expansion of geothermal energy use.

During the creation and integration of a health care system based on medical knowledge graphs, it is necessary to review and select the vocabularies and definitions that best fit the information requirements of the system being developed. This implies the reuse of medical knowledge graphs; however, full importation of knowledge graphs is not a tractable solution in terms of memory requirements. In this paper we present a modularization-based method for knowledge graph reuse. A case study of graph reuse is presented by transforming the original model into a lighter one.

This paper is motivated by three main assumptions: (1) The currently dominant forms of knowledge representation (natural language text, formulas, graphics) are suboptimal to facilitate knowledge transfer between control engineering sub-fields and into potential application domains. (2) Formal knowledge representation (in general) is a promising supplementary approach, but (3) the established technologies so far have failed to be significantly adopted for formal representation of control engineering knowledge. Thus, after briefly reviewing existing representation methods, we introduce our own approach: The Python based imperative representation of knowledge (PyIRK) and its application to formulate the Ontology of Control Systems Engineering (OCSE). One of its main features is the possibility to represent the actual content of definitions and theorems as nodes and edges of a knowledge graph, which is demonstrated by selected elements from Lyapunov theory. While the approach is still experimental, the formalization already allows to apply methods of automated quality assurance and a semantic search mechanism. The current feature set of the framework is demonstrated on various examples before directions for further developments are discussed.

This study assesses the impact of incorporating an adaptive learning mechanism into an agent-based model (ABMS) simulating behavior on a university campus during the course of a pandemic outbreak, with a particular case on the COVID-19 pandemic. The aim is to reduce overcrowding and infections on campus through the use of Reinforcement Learning (RL). Our findings indicate that RL is a viable approach for effectively representing agents’ behavior within this context. The results reveal specific temporal patterns of overcrowding violations. While our study successfully mitigated campus crowding, it had limited influence on altering the course of the epidemic. This highlights the necessity for comprehensive epidemic control strategies that consider the role of individual decision-making influenced by adaptive learning, along with the implementation of targeted interventions. This research significantly contributes to our understanding of adaptive learning within complex systems and offers valuable insights for shaping future public health policies in similar community settings. Future research directions encompass exploring various parameter settings and updating representations of the disease’s natural history.

Evidence-based data for endometriosis management are limited. Experiments are excluded without adequate animal models. Data are limited to symptomatic women and occasional observations. Hormonal medical therapy cannot be blinded if recognised by the patient. Randomised controlled trials are not realistic for surgery since a variable disease with low numbers. Each diagnosis and treatment is an experiment with an outcome and experience is Bayesian updating from the past. If the experience of many is similar, this has more value than an opinion. The combined experience of a group of endometriosis surgeons was used to discuss problems in the management of endometriosis. Considering endometriosis as several genetically/epigenetically different diseases is important for medical therapy. Imaging cannot exclude endometriosis and diagnostic accuracy is limited for superficial lesions, deep lesions, and cystic corpora lutea. Surgery should not be avoided for emotional reasons. Shifting infertility treatment to IVF without considering fertility surgery is questionable. The concept of complete excision should be reconsidered. Surgeons should introduce quality control and teaching should move to explain why it is done. The perception of information has a personal bias. These are the major problems in the management of endometriosis identified by the combined experience of endometriosis surgeons.

Spatio-temporal aspects of data lead to critical information. Sensors capture data at all scales continually so it is imperative that useful information be extracted ubiquitously and regularly. Location plays a vital part by helping understand relations between datasets. It is crucial to link developmental works with spatial attributes and current challenge is to create an open platform that manages real-time sensor data and provides critical spatial analytics atop expert domain knowledge provided in the system. That is a two-faced problem where the solution tackles not only data from multiple sources but also runs data management platform, a spatial data infrastructure(SDI) as backbone framework able to harness sensor web(SW). The paper proposes development of such a globally shared open spatial expert system(ES), SmaCiSENS, a first of a kind geo-enabled knowledge based(KB) ES for multiple fields, smarter cities to climate modeling. SmaCiSENS is integration of SW and SDI with domain KB on data and problems, ready to infer solutions. The paper describes an architecture for semantic enablement for SW, SDI; connect interfaces, functions of SDI and SW, and sensor data application program interfaces (APIs) to better manage climate modeling, geohazard, global changes, and other vital areas of attention and action.

This article presents a hybrid approach to assessing students’ foreign language proficiency in a cyber-physical educational environment. It focuses on the advantages of integrated assessment of student knowledge by considering the impact of automatic assessment, learners’ independent work, and their achievements to date. An assessment approach is described using the mathematical theory of fuzzy functions, which are employed to ensure fair evaluation of students. The largest possible number of students, whose re-evaluation of test results will not affect the overall performance of the student group, is automatically determined. The study also models the assessment process in the cyber-physical educational environment through the formal semantics of calculus of context-aware ambients (CCA).

The technical capabilities of modern Industry 4.0 and Industry 5.0 are rather vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnections and communications among heterogeneous devices. Smart cities are established with sophisticated designs and control for seamless Machine-to-Machine (M2M) communication to optimize resources, costs, performances, and energy distribution. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, it encompasses quite a few challenges for devices that lack compatible and interoperable designs. Conventional solutions are restricted to limited domains or rely on engineers to design and deploy translators for each pair of ontologies. This is a costly process in terms of engineering efforts and computational resources. The issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontology meta-data and structural information. The model finds matches between two distinct ontologies using the Natural Language Processing (NLP) approach for learning linguistic contexts. Then, by visualizing the ontology network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device's message formulation. Finally, it can align entities of both ontology graphs similar in context and structure. Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware efforts.

This paper considers the question of what kind of knowledge is produced by deep learning. Ryle’s concept of knowledge how is examined and is contrasted with knowledge with a rationale. It is then argued that deep neural networks do produce knowledge how, but, because of their opacity, they do not in general, though there may be some special cases to the contrary, produce knowledge with a rationale. It is concluded that the distinction between knowledge how and knowledge with a rationale is a useful one for judging whether a particular application of deep learning AI is appropriate.

The article addresses the issue of reducing the carbon footprint in the formula of the European Union’s New Green Deal using nuclear energy introduced in place of coal. It contains an analysis of the possibilities offered by currently developed concepts of using nuclear energy in the context of existing energy infrastructure in Poland. It indicates the compatibility of coal and nuclear technologies for generating electricity and heat, considering only those with the highest level of technological readiness. The aim of the article is to identify the energy transformation in Poland using nuclear technologies and acquiring, creating and developing knowledge on the subject. It is assumed that the future of coal-nuclear transformation depends on determining where investment processes could take place in Poland, demonstrating the power that can be replaced through transformation, and indicating appropriate coal CPP (Carbon Power Plant) and nuclear NPP (Nuclear Power Plant) technologies with which a roadmap for the development of Polish nuclear energy can be created. The direction of potential actions is shown, whose coordination and implementation requires cooperation between many entities with appropriate knowledge and experience. For the purposes of this article, preliminary research was conducted on the possibilities offered by currently developed concepts in the context of existing energy infrastructure in Poland, its transformation, and the necessary social capital and its sources. The considerations did not take into account power plants and combined heat and power plants that have been liquidated or the use of their remaining infrastructure.

The realization of the third-generation artificial intelligence (AI) requires the evolution from perceptual intelligence to cognitive intelligence, where knowledge graphs may not meet the practical needs anymore. Based on the dual channel theory, cognitive graphs are established and developed through coordinating the implicit extraction module and the explicit reasoning module as well as integrating knowledge graphs, cognitive reasoning and logical expressions, which have achieved successes in multi-hop question answering. It is desired for cognitive graphs to be widely used in advanced AI applications such as large-scale knowledge representations and intelligent responses, promoting the development of Al dramatically. This review discusses cognitive graphs systematically and elaborately, including basic concepts, generations, theories and technologies. Moreover, we try to predict the development of cognitive intelligence in the short-term future and further enlighten more researches and studies.

Whilst prior works have characterised the affordances of prototyping methods in terms of generating knowledge about a product or process, the types, or ‘dimensions’ of knowledge towards which they contribute are not fully understood. In this paper we adapt the concept of ‘design domains’ as a method to interpret, and better understand the contributions of different prototyping methods to design knowledge in new product development. We first synthesise a set of ten dimensions for design knowledge from a review of literature in design-related fields. A study was then conducted in which participants from engineering backgrounds completed a Likert-type questionnaire to quantify the perceived contributions to design knowledge of 90 common prototyping methods against each dimension. We statistically analyse results to identify patterns in the knowledge contributions of different methods. Results reveal that methods exhibit significantly different contribution profiles, suggesting different methods to be suited to different knowledge generation. Thus, this paper indicates potential for new methods, methodology and processes to leverage such characterisations for better selection and sequencing of methods in the prototyping process.

Stenting is a common method for treating atherosclerosis. A metal or polymer stent is deployed to open the stenosed artery or vein. After the stent is deployed, the blood flow dynamics influence the mechanics by compressing and expanding the structure. If the stent does not respond properly to the resulting stress, vascular wall injury or re-stenosis can occur. In this work, Discrete Multiphysics is used to study the mechanical deformation of the coronary stent and its relationship with the blood flow dynamics. The major parameters responsible for deforming the stent are sort in terms of dimensionless numbers and a relationship between the elastic forces in the stent and pressure forces in the fluid is established. The blood flow and the stiffness of the stent material contribute significantly to the stent deformation and affect the rate of deformation. The stress distribution in the stent is not uniform with the higher stresses occurring at the nodes of the structure.

In this paper, we introduce a non-invasive approach for monitoring bridge infrastructure with ground-based interferometric radar. This approach is called the mirror mode, since it utilises the flat surface of the bridge underside as a mirror to reflect the signal to a corner reflector on the ground placed opposite of the radar sensor. For proving the feasibility of this approach, a measurement campaign has been carried out at an exemplary bridge in Karlsruhe (Germany) including a radar sensor in mirror mode, a second radar sensor in the default mode and a laser profile scanner. We investigate the potential of this approach to monitor the bridge displacement in vertical direction and compare the results with the two other sensors. The derived results reveal the potential for monitoring bridge infrastructure. Finally, we propose further research aspects of this approach to analyse its capabilities and limitation in the context of non-invasive infrastructure monitoring.

An exposition of entropy applications to highlight some potential entropic applications to artificial intelligence and knowledge transfer. Entropy is a basic idea in artificial intelligence that is applied to many different tasks, such as reinforcement learning, data compression, and decision-making. By quantifying uncertainty and information content, it helps artificial intelligence models provide predictions and decisions that are well-informed. Therefore, this paper shines on spotlighting the significance of entropy to attract the attention of artificial intelligence research community to entropy as a powerful tool to advance artificial intelligence.The significance of knowledge transfer (KT), particularly intergenerational knowledge transfer (IGT), in knowledge management is also covered in this paper. To quantify the complexity of knowledge distribution inside an organisation and assess the efficacy of knowledge transfer (KT) initiatives, the notion of knowledge entropy (KE) is presented. Additionally, the KT model—which is based on the conceptions of tacitness and information content—is introduced. It combines personalisation and codification methodologies. Future research directions are offered alongside a few difficult open problems.

Cadmium (II) contamination in the environment is an emerging problem due to its acute toxicity and mobility, so it is very urgent to remove this species from industrial wastewater before it is discharged into the environment. Thus, a starch-based activated carbon (AC) with a specific surface area of 1600 m2g-1 is used as an adsorbent for the capturing of toxic Cadmium (II) ions from synthetic solution. The sorbent is characterized by BET, SEM, TEM, XRD, FT-IR, TGA, and zeta potential. The maximum uptake (284 mg g-1) of Cadmium (II) ion is obtained at pH 6. The thermodynamics parameters like ∆G, ∆H, ΔS are found to be -17.42 kJmol-1, 6.49 kJ mol-1, and 55.66 Jmol-1K-1 respectively, revealing that the adsorption mechanism is endothermic, spontaneous, and feasible. The experimental data follows the D-R and Langmuir models well. The mass transfer is controlled by pseudo 2nd order kinetics. Furthermore, the density functional theory simulations demonstrate that the activated carbon strongly interacted with the Cd (II) ion through its various active sites. The adsorption energy noted for all interactive sites is highly negative (-0.45 eV to -10.03 eV), which shows that the adsorption process is spontaneous and stable which is in agreement with the experimental thermodynamics analysis.

The discharge of industrial effluents containing toxic heavy metals in water sources has serious consequences for human health and the environment, and biosorption appears to be an environmental-friendly and cost-effective alternative that can be used for wastewater treatment. The use of different types of agricultural waste as biosorbents for the removal of toxic heavy metals, although an alternative, is quite difficult to apply in practice because these wastes have many other uses. Based on these considerations, in this study, soybean biomass (SB), soybean waste biomass obtained from oil extraction (SBW) and biochar obtained from soybean waste pyrolysis (BC-SBW) were testes as biosorbents form the removal of Pb(II) and Cd(II) ions, in batch systems. Under optimal conditions (pH = 5.4, 4.0 g biosorbent/L, room temperature (25 ± 1 C), contact time = 180 min), the biosorption capacity increase in the order: SB < SBW < BC-SBW for both metal ions (Pb(II) ions (69.43 mg/g < 99.81 mg/g < 116.83 mg/g) and Cd(II) ions (25.63 mg/g < 36.12 mg/g < 49.10 mg/g)), indicating that BC-SBW has the highest efficiency in removing toxic heavy metals. In addition, experiments on wastewater samples have shown that, in addition to significant reducing the content of heavy metals, BC-SBW also significant improved other quality indicators (such as: pH, TSM, CCO-Cr, CBO5), compared to the other biosorbents (SB and SBW). Quantitative evaluation of the biosorptive performance of each biosorbent (SB, SBW and BC-SBW) shows that BC-SBW has a real chance of being used on an industrial scale for wastewater treatment. All these aspects allowed the development of a circular approach for the use of soybean biomass in the removal processes of toxic heavy metals. This approach minimizes the shortcomings of using biomass as a biosorbent and increase the chance of using these materials in industrial practice.

Plug and abandonment (P&amp;A) operations demand valuable time and resources with operational procedures and materials to establish the well barrier element. This study proposes the use of a water-based fluid as a liquid well barrier element for temporary abandonment, based on esti-mates of its lifespan and survival probability on downhole conditions acquired through acceler-ated life tests. For that, the water-based formulation was tested and exposed to 95, 110, 140 and 150°C, for time intervals ranging from 1 to 10 days. After temperature exposure, the fluid prop-erties were verified and failure was detected by accounting for any deterioration in rheological parameters and/or a substantial increase in filtrate volume. Statistical analysis of failure data was performed in R software using Weibull Model, and fluid average lifespan and survival probability were estimated for P&amp;A temperatures. The results obtained demonstrates that the degradation of the fluid only was observed for 140 and 150°C and is mostly related to the CMC deterioration. The reliability metrics shows that until 80°C the fluid is a promising alternative for temporary abandonment, with no need of monitoring. For higher downhole temperatures, theses metrics should be considered to define the maximum time of operation and requirements for monitoring management in the operation design.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

The Infectious and Tropical Diseases Department of University of Brescia organized free rapid screening tests for HIV and HCV as part of the Fast Track City commitment. A cross-sectional study was conducted, consisting of an anonymous, multiple-choice questionnaire that was administered to individuals who underwent the screening or consultation. The study aimed to compare knowledge and attitudes towards HIV and HCV between age groups (18-40 vs. >40) and sexual orientations (heterosexual vs. LGBTQ+). Overall, 333 questionnaires were completed. Individuals over the age of 40 and those who identified as heterosexual had the lowest level of knowledge about HIV transmission (7.9% and 30% respectively), undetectable=untransmittable (19.7% and 39.4% respectively) and PrEP (9.2% and 18.2% respectively). They were also less willing to cohabit with people with HIV (PLHIV). Only 9.6% of the population demonstrated high levels of knowledge for both HIV and HCV. People over the age of 40 had the largest gaps in knowledge and would benefit the most from educational and prevention interventions.

Scientific social networking sites like Researchgate or Academia.edu have become part of the work practice of academic researchers. These digital platforms have been designed precisely to encourage the exchange of knowledge between scholars and to help the expansion of collaborative networks among them. Even if studies on this topic have multiplied in recent years, there is a dearth of research on the actual impact of these platforms on scientific production. The goal of this study is to investigate the relation between the attitude of researchers towards scientific social networks and the use of knowledge in their scientific work. Data from users of the scientific social networking site Researchgate were collected. A total of 143 valid responses were received and structural equation modeling was applied for data analysis. Findings of this study confirmed that researchers use knowledge obtained from scientific social networking sites both incorporating it within their research products and to acquire new competences. In particular approaching the platform as a scientific community with a shared language and a shared vision was found to have a positive impact on knowledge use. To the best knowledge of the authors this is the first study investigating the actual use of knowledge from scientific social networking sites by academic researchers. Results help to understand the impact of these platforms on the work practice of a strategic sector like scientific research.

To understand forest dynamics under today’s changing environmental conditions, it is important to analyze the state of forests at large scales. Forest inventories are not available for all regions, so it is important to use other additional sources of information, e.g. remote sensing observations. Increasingly, remotely sensed data based on optical instruments and airborne LIDAR are becoming widely available for forests. There is great potential in analyzing these measurements and gaining an understanding of forests state. In this work, we combine the new generation radiative transfer model mScope with the individual-based forest model FORMIND to generate reflectance spectra for forests. Combining the two models allows us to account for species diversity at different height layers in the forest. We compare the generated reflectances for forest stands in Finland, in the region of North Karelia, with Sentinel-2 measurements. We investigate which level of forest representation gives the best results. For the majority of the forest stands, we generated good reflectances with all levels forest representation compared to the measured reflectance. Good correlations were also found for the vegetation indices (especially NDVI with R²=0.62). This work provides a forward modelling tool for relating forest reflectance to forest characteristics. With this tool it is possible to generate a large set of forest stands with corresponding reflectances. This opens the possibility to understand how reflectance is related to succession and different forest conditions.

The United Nations' sustainable development goals have underscored the importance of enhancing supply chain sustainability while appropriately distributing orders. This study proposes a novel framework involving Z-number, game theory, indifference threshold-based attribute ratio analysis (ITARA), and combined compromise solution method (CoCoSo) to evaluate the sustainability of suppliers and order allocations. To better reflect the decision makers’ current choices for the sustainability of assessed suppliers and order allocations; enhance the comprehensiveness of decision-making, the importance parameter of the supplier is obtained through game theory objectively for transforming supplier performance into order allocation performance. The Z-numbers are involved in ITARA (so-called ZITARA) and CoCoSo (so-called ZCoCoSo) to overcome the issue of information uncertainty in the process of expert evaluation. ZITARA and ZCoCoSo are applied to assess the objective weight of criteria and the ranking of assessed order allocations, respectively. Afterwards, a case study of China’s company is illustrated the utility of the proposed framework and inform their decision-making process regarding the orders to be assigned to the assessed suppliers.

This comprehensive review elucidates the profound relationship between the human microbiome and breast cancer managrement. Recent findings highlight the significance of microbial alterations in tissues, such as the gut and the breast, and their role in influencing breast cancer risk, development, progression, and treatment outcomes. We delve into how the gut microbiome can modulate systemic inflammatory responses and estrogen levels, thereby impacting cancer initiation and therapeutic drug efficacy. Furthermore, we explore the unique microbial diversity within breast tissue, indicating potential imbalances brought about by cancer and highlighting specific microbes as promising therapeutic targets. Emphasizing a holistic One Health approach, this review underscores the importance of integrating insights from human, animal, and environmental health to gain a deeper understanding of the complex microbe-cancer interplay. As the field advances, strategic manipulation of the microbiome and its metabolites presents innovative prospects for enhancing cancer diagnostics and therapeutics. However, rigorous clinical trials remain essential to confirm the potential of microbiota-based interventions in breast cancer management.

Coal combustion in thermal power plants releases ash. Ash is reported to cause different adverse health hazards in humans and other organisms. Owing to the presence of radionuclides, it is also considered as a potential radiation hazard. In this study, based on the surface radiation measurements and relevant ancillary data, expected radiation risk zones were identified with regard to the human population residing near the Thermal Power Plant. With population density as the risk determining criteria, about 20% of the study area was at ‘High’ risk and another 20% of the study area was at ‘Low’ risk zone. The remaining 60% was under medium risk zone. Based on the findings remedial measures which may be adopted have been suggested.

Biodiversity plays an important role in maintaining the ecological balance of the earth. The study of traditional knowledge related to biological resources is a hot issue in the field of international biodiversity conservation. Xishuangbanna is a key area of biodiversity and a cultural hotspot of international significance. According to the Technical Regulation for Classification, Investigation, and Inventory of Traditional Knowledge Relating to Biological Diversity issued by the Ministry of Ecology and Environment, we investigated and catalogued the traditional knowledge related to biodiversity of the Jino people who have lived in Xishuangbanna for generations, and collected 490 entries of traditional knowledge related to biodiversity of the Jino people. Drawing on the traditional knowledge diversity index calculation method proposed by Wang Guoping, the overall traditional knowledge α-diversity index of the Jino people is 0.63, indicating that the richness of the traditional knowledge of the Jinuo people is relatively high. The traditional culture related to biodiversity, the traditional knowledge related to agricultural genetic resources, and the traditional technology related to the sustainable utilization of biological resources are relatively rich and diverse. The diversity index is 0.86, 0.82 and 0.79 respectively. In addition, Xishuangbanna has invested a lot of energy in biodiversity protection, including the establishment of nature reserves, botanical gardens, zoos, ecological tea gardens and other species reserves, the promulgation of laws and policies related to biodiversity protection, and has achieved remarkable results in in-situ protection and ex-situ protection. On the basis of analyzing the progress and achievements of biodiversity conservation in Xishuangbanna, this study points out that Xishuangbanna faces challenges such as the loss of traditional knowledge, insufficient conservation efforts, and great changes in land use, and puts forward corresponding suggestions.

Culture signifies the customs, language, ceremonies, Indigenous Medical Knowledge (IMK) and other material objects of a tribe. These and many other features are a representation of identity for many Zambian communities. IMK practices are skills inherent by certain individuals in a tribal grouping who are chosen by the spirits (gods) or trained by those who practice. This intrinsic skill is in danger of diminishing as a result of modern medication. This article attempts to understand the role of traditional healers (also known as asing’anga in local language) in preservation of IMK in Chibombo District of Zambia. In so doing, the paper examined methods of preservation used by asing’anga and the challenges faced. Using hermeneutic phenomenology and linear snowball sampling, findings revealed that preservation was done through training. The study found unwillingness, expiration, and spirituality as challenges in the process of preserving IMK. The study recommended that the Lenje Cultural Association consider documenting and disseminating information to the local people by showcasing what is available in the Mukuni Culture Village Museum and Library. This was seen as a way through which many youths would be encouraged to appreciate the indigenous values in their culture.

In an innovation driven business environment, cross-border access to resources is important for companies to improve innovation capabilities and development performance. Based on the previous research, it shows that there are barriers to cross domain communication among alliance firms because of the restriction of multidimensional ties and dyads. Simmelian ties, as a form of alliance network with ternary connections, it effectively restrained opportunism and self-interest in the cooperation process and take a crucial role to evaluate innovation related performance in corporation. Based on the theory of Simmelian, this paper builds a theoretical framework and proposes corresponding research hypotheses between Simmelian ties and enterprise innovation performance. After designing questionnaires, collecting data and conducting empirical analysis to test theoretical models and hypotheses. Results have shown that: (1) Simmelian ties generally have a positive impact on enterprise innovation performance. (2) Knowledge capturing and knowledge integration play a partial intermediary role between Simmelian ties and enterprise innovation performance, and the mediating chain formed by the two variables plays a serial mediating role in the effect. (3) Network routines significantly positively moderates the relationship between Simmelian ties and knowledge capturing. And also, the positive relationship between Simmelian ties and enterprise innovation performance is also actively moderated by network routines. The conclusion of this study is meaningful for companies to establish of Simmelian ties, improve knowledge management capabilities and further promote enterprise innovation performance.

For effective warehouse management, various criteria should be taken into consideration in terms of inventory management, demand, transportation distance, usage area, delivery-waiting times, and minimizing the costs that may arise from them. Because, the purpose of the product assignment problem is to minimize the total transport distance during storage and to reduce the space required for product assignment. However, the storage location assignment problem (SLAP) occurs simultaneously with production planning activities in real life. In this study, an integrated mixed-integer nonlinear (MINLP) mathematical model is proposed, which includes the assignment of products to storage areas and production planning, using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) class based policy for storage systems where multiple criteria are important. In the TOPSIS class-based policy, weight and waiting time criteria are taken into account, as well as the COI (Cube-per-order index index), which is widely used in the literature. In addition, a decision support system flow has been proposed that takes into account the single criterion, multiple criteria and COI index criteria of the developed model and provides its integration with different product assignment policies. Detailed literature research; It has been revealed that the proposed mathematical model and decision support system flow have the feature of applying an existing field of science to a new problem.

Balancing the efficiency and robustness of food systems is a well-known trade-off process. Over-optimization of efficiency may lead to excessive use of resources. On the other hand, under-optimization of efficiency may lead to excessive waste of resources. Using non-parametric methods can help in developing actionable frameworks through which decision-makers and stakeholders can effectively balance and improve food systems' efficiency and robustness. Under this context, this paper presents a new structured approach integrating two well-suited non-parametric methods for analyzing and balancing the interconnection between the efficiency and robustness of food systems. This approach consists of three main steps. First, the slacks-based data envelopment analysis (SBM-DEA) is utilized to analyze the efficiency of five food robustness dimensions. Second, the weighted efficiency of these dimensions is computed and analyzed to provide an insight into their influence on the food system efficiency. Finally, two search methods were developed to identify opportunities for improving efficiency and robustness. The outcomes of these methods have been analyzed and validated to ensure their effectiveness and viability. The operational and mathematical properties of the proposed approach were evaluated using data from thirty-seven food-producing countries. The weights of the robustness dimensions in this study were determined using the analytic hierarchy process (AHP) method. The input matrix of the AHP was constructed using expert judgment and consultations with stakeholders. The results of this study have shown an appropriate level of applicability and effectiveness of the proposed approach. While the first search method provided valuable insights, the second method proved to be more effective in identifying the sources of inefficacy of robustness dimensions. Several conclusions have been drawn and reported with some recommendations for future research works.

Urban air pollution is a significant threat to both the environment and public health. Primarily originating from traffic emissions, particulate matter is identified as the most prevalent and toxic pollutant in cities. In recent years, street vegetation has emerged as a nature-based solution to improve urban air quality. However, to achieve this goal, proper placement of vegetation is crucial. This study critically investigates how different street tree placement strategies affect the local air quality, based on a model calibrated using existing air quality measurements from one of the most polluted streets in Glasgow. We then test the local air quality effects of tree orientation, spacing, shape, porosity, size, species, and wind directions. The findings suggest that trees effectively enhance air quality, particularly when implemented on the leeward side of the street. To achieve optimal effectiveness, the spacing of trees require careful alignment with the aspect ratio, as haphazard planting could lead to unfavorable results. The insights on improving air quality in Glasgow's street canyons, complements the objectives of the ‘Glasgow Avenues Programme’ and can be included in its Low Emission Zones to co-benefit the environment and the wellbeing of the local community.

Since sensor-based perception systems are used in autonomous vehicle applications, validating such systems is imperative to guarantee the robustness of the systems before they are being put to use. In this study, a comprehensive corruption-related simulation-based robustness verification and enhancement process for sensor-based perception systems is proposed. Firstly, we present a methodology and scenario-based corruption generation tools for creating diverse simulated test scenarios that can analogously represent real-world traffic environments, especially considering corruption types related to safety concern. Then, an effective corruption similarity filtering algorithm is proposed to remove corruption types with high similarity and identify the representative corruption types to represent all considered corruption types. As a result, we can generate efficient corruption-related robustness test scenarios with less testing time and good scenario coverage. Subsequently, we perform the vulnerability analysis of object detection models to identify model weaknesses and construct an effective training dataset for model vulnerability enhancement. This enhances the tolerance of object detection models to weather and noise-related corruptions, ultimately improving the robustness of the perception system. We employ case studies to demonstrate the feasibility and effectiveness of the proposed robustness verification and enhancement procedures. Additionally, we explore the impact of different "similarity overlap threshold" parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

With the continuous increase in ship size combined with the generally slower increase in the size of waterways, the need for the prediction of ship maneuvering in shallow waterways continues to attract attention from the international scientific community. Ship behavior in shallow water is relevant in seabed effects that result in changing the hydrodynamic forces acting on the ship. In this study, the maneuvering characteristics of container ship with four degrees of freedom in shallow water are analyzed. The Reynolds-averaged Navier Stokes approach in Ansys Fluent code is used to produce the maneuvering coefficients through the simulations of forward running, static drift, static heel, circular motion, the combined motions, and the pure roll motion of the Kriso container ship. The maneuvering characteristics of the ship are estimated for evaluating the ship behaviors in shallow water condition. The obtained results show that the roll has a significant decrease and the ship’s turning diameter has a significant increase when the ship operates in the shallow waterway. The prediction of maneuvering characteristics of the ship are in good agreement with those of free running model tests, indicating that the numerical simulation based on the Computational Fluid Dynamics method has compromising capability to predict the maneuvering derivatives and the 4-DOF ship maneuvering motion in shallow water as well.

In an age of unprecedented global complexity, interconnectedness, and urgency, the inadequacy of traditional, reductionist models of knowledge production has become glaringly apparent. This pioneering, landmark article offers a sweeping, paradigm-shifting exploration of the revolutionary emergence of "Mode 4" knowledge production - a fundamental reconceptualization of the epistemological, organizational, and methodological foundations of the research enterprise. Drawing on cutting-edge theoretical frameworks and a vast corpus of rigorous empirical evidence, this work argues that Mode 4 represents a transformative leap towards a more collaborative, transdisciplinary, and adaptive approach to knowledge creation - one that holds the potential to catalyze a profound and lasting transformation in the way we conceive of, organize, and mobilize research to address the complex, interconnected challenges facing our world. At the heart of this paradigm shift lies the groundbreaking "decuple helix" framework, which expands the scope of stakeholder engagement and knowledge co-creation to incorporate a comprehensive range of actors, from academia and industry to marginalized communities, the natural environment, and international organizations. This article delves deeply into the multifaceted roles and invaluable contributions of this diverse array of stakeholders, demonstrating how their active integration can unlock the transformative power of collaborative, values-oriented research and innovation. Furthermore, the paper provides a comprehensive example of how Mode 4 knowledge production concepts could be implemented using cutting edge innovationology research. By drawing on a rich tapestry of theoretical foundations, including complexity theory, quantum physics, humanities, social sciences, spirituality, and the arts, innovationology exemplifies the transdisciplinary ethos at the core of this paradigm shift. The article delves deeply into the collaborative co-creation, iterative and adaptive methodologies, and holistic, values-driven vision that define this groundbreaking transdisciplinary science. However, this work also candidly explores the significant institutional, methodological, equity-related, and scalability challenges that continue to hinder the widespread adoption and implementation of the Mode 4 and decuple helix frameworks. In doing so, it charts a course forward, outlining a comprehensive set of practical implications and recommendations to address these barriers and unlock the transformative potential of these emerging paradigms. Ultimately, this article offers a sweeping, cohesive, and visionary analysis of the revolutionary emergence of Mode 4 knowledge production and the decuple helix framework - positioning itself as a landmark contribution that has the potential to catalyze a profound transformation in the way we conceive of, organize, and mobilize research for a sustainable and equitable future. With its groundbreaking insights, bold vision, and rigorous interdisciplinary foundation, this work stands as a clarion call for a new era of collaborative, transdisciplinary knowledge production that can truly address the complex, interconnected crises facing our world.

This study proposes a novel hybrid machine learning approach for modeling of rainfall-induced shallow landslides. The proposed approach is a combination of an instance-based learning algorithm (k-NN) and Rotation Forest (RF), state of the art machine techniques that have seldom explored for landslide modeling. The Lang Son city area (Vietnam) is selected as a case study. For this purpose, a spatial database for the study area was constructed, and then, was used to build and evaluate the hybrid model. Performance of the model was assessed using Receiver Operating Characteristic (ROC), area under the ROC curve (AUC), success rate and prediction rate, and several statistical evaluation metrics. The results showed that the model has high performance with both the training data (AUC = 0.948) and the validation data (AUC = 0.848). The results were compared with those obtained from soft computing techniques i.e. Random Forest, J48 Decision Trees, and Multilayer Perceptron Neural Networks. Overall, the performance of the proposed model is better than those obtained from the above methods. Therefore, the proposed model is a promising tool for landslide modeling. The research result can be highly useful for land use planning and management in landslide prone areas.

The worldwide population is undergoing a fundamental change in its age structure, which challenges the health and social services system. The need to migrate towards a more person-centered and coordinated model of care that supports the optimization of abilities and capacities for older people has to be matched. In this sense, eHealth technologies can play a fundamental role. In this paper, through a questionnaire-based data collection on 30 primary (older people) and 32 secondary (informal caregivers) end-users, we share the sustainable way to develop a product optimizing the user experience and ensuring adoption. Both older adults and caregivers consider the use of smartphone and smartwatch to be very important, but also the use of digital devices for healthcare can be helpful. Seniors care about self-monitoring of health parameters using wearable devices, regardless of their health status, and would like to be included in the process of making good health decisions, because they need to feel in control of their healthcare process. Digital solutions in health and care can support the wellbeing of older adults in many areas of their daily lives at home and in their communities, but only if such innovation is designed around the natural voice of the intended target.

Acute low back pain (LBP) stands as a leading cause of activity limitation and work absenteeism, and its associated healthcare expenditures are expected to become substantial when acute LBP develops into a chronic and even refractory condition. Therefore, early intervention is crucial to prevent progression to chronic pain whose management is particularly challenging and for which the most effective pharmacological therapy is still controversial. Current guideline treatment recommendations vary and are mostly driven by expertise with opinion differing across different interventions. Thus, it is difficult to formulate evidence-based guidance when relatively few randomized clinical trials did explore the diagnosis and management of LBP while employing different selection criteria, statistical analyses, and outcome measurements. This narrative review aims to provide a critical appraisal of current acute LBP management by discussing the unmet needs and areas of improvement from bench-to-bedside and proposes multimodal analgesia as the way forward to attain an effective and prolonged pain relief and functional recovery in patients with acute LBP.

Clinical trials, by contributing to the development of diagnostics and to the search for modern, more effective, and safer therapies, have become one of the most important elements of the healthcare system. The aim of this study was to assess Poles' knowledge and beliefs about clinical trials. Methods: The survey was conducted among Poles aged 18 and over with the help of external company, Ariadna, which is an independent research panel. The questionnaire contained 22 questions, of which 13 questions concerned beliefs and attitudes towards clinical trials. Results: One thousand and seventy-nine participants took part in the study (N = 1079). The mean age of respondents was 44.96 years (SD = 16.30). Slightly more women (N = 568, 52.6%) than men (N = 511, 47.4%) took part in the study. Among the respondents, 86.5% (N = 933) were aware of clinical trials. The main sources of information about clinical trials were the media (53.8%) including the Internet (N= 355, 32.9%), TV (N = 175, 16.2%), press (N = 30, 2.8%) and radio (N = 21, 1.9%). 43.2% (N = 466) of respondents reported little knowledge of clinical trials, while more than three quarters (N = 805, 75.2%) said they would like to learn more about clinical trials. Most respondents (N = 879, 81.4%) agreed with the statement that participation in a clinical trial is completely voluntary, and more than half (N = 580, 53.7%) agreed with the statement that hospitals participating in clinical trials provide better healthcare. The statement that the results of clinical trials are made available to the public was disagreed with by 37.2% (N = 402) of participants. Only 30.3% (N = 327) of participants agreed that clinical trials should be conducted with children. Conclusions: Poles rate their knowledge of clinical trials as low and would like to learn more. To reach the largest possible group of Polish patients, parallel educational activities on multiple levels may have the best effect. Poles' knowledge of clinical trials is mainly based on commercial sources. Further studies in different groups, i.e., clinical trial patients and investigators, are needed.

This article aims to highlight the intricate interplay between psychological knowledge and local wisdom. Ethiopia is a diverse and multicultural country, with a rich diversity of languages, cultures, and traditions. Ethiopian local stories, proverbs, and poems have long served as vessels of wisdom, offering valuable life lessons and insights into human behavior. This article seeks to uncover the local wisdom through psychological lenses by demonstrating the psychological richness embedded within Ethiopian local stories, proverbs, and poems. Thus, valuable insights can be found by drawing connections between these traditional forms of wisdom and contemporary psychological theories. Moreover, researchers can foster a more inclusive and culturally sensitive approach to understanding and addressing psychological issues by bridging the gap between academic knowledge and local wisdom. This symbiotic relationship has the potential to expand the boundaries of psychological knowledge and encourage innovative ideas and principles, ultimately enriching both domains.

Artificial insemination (AI) is considered as the most efficient feasible tool to improve the dairy cows’ profitability and productivity. In bovines, AI helps to promote genetic improvement and can positively impact the rural economy. The achievement of education in rural populations was one of the Millennium Development Goals (MDGs), made by the United Nations. During the last World Summit on Sustainable Development (WSSD), it was concluded that most of the world’s poor, illiterate and undernourished population lives in rural areas; therefore, access to education is considered one of the greatest challenges for governments in countries with developing economies. To determine the effects of a theoretical-practical training program related to rural management and leadership, AI, and bovine genetic improvement, on the perception and level of knowledge in a rural Colombian population that has been affected by the armed conflict. Phase 1. Included the practical and theoretical training of undergraduate animal sciences students in three dimensions rural management; leadership (D-ML), AI in bovines (D-AI), and genetic improvement (D-GI) after training students were evaluated and selected according to their skills, characteristics, solidarity skills, and their social sensitivity was identified. Phase 2. Included the socioeconomic characterization and training of rural residents by students and field experts in three dimensions D-ML, D-AI, and D-GI. After training we evaluated the perception and level of knowledge before and after training in 63 rural residents using an evaluative instrument. The data before and after (post-test) of the theoretical-practical training, were analyzed to obtain a statistical significance. The perception of knowledge in rural residents was low for general knowledge (2.48±0.76, p ˂ 0.05), D-ML (2.89±1.18, p ˂ 0.05) and D-AI and D-GI (2.17±0.83, p ˂ 0.05). On the other hand, before the theorical-practical training the level of general knowledge was 45.9%, however, after the training increased at 77.6% (p<0.01), while the level of knowledge for the D-AI was increased from 38.5 % to 80.6 % (p ˂ 0.05) after training. Regarding the level of knowledge for the D-GI pre-training was 50.8 and increased to 73.0% (p<0.01) then after. Finally, the level of knowledge for the D-ML was increased (p<0.01) from 54.8 before training to 75% after training. Altogether, the rural extension programs contribute to the closing of knowledge gaps in relation to the use of reproductive biotechnologies and bovine management in rural areas affected by armed conflict.

Objectives: to assess the knowledge level of parents from the central region of Saudi Arabia about oral health and care of preschool children and its relation with sociodemographic variables, parents‟ self-perception toward their dental health, importance of teeth and frequency of dental visits. Methods: a random sample of 754 parents participated in this cross-sectional study and completed an internationally accepted questionnaire. Chi square test and logistic regression analysis were used to analyze the data (p<0.05). Results: mean knowledge score of the parents was 4.8 (out of 11). Less than 20% of the parents were knowledgeable about the best position for tooth brushing, the concentration of fluoride in a child‟s toothpaste, timing of first dental check-up, and best time to give a sugary snack. Mothers, parents with high educational level and family income, parents with positive attitude towards teeth and excellent self-perception of their dental health were significantly more likely to score higher (p<0.05). Conclusions: knowledge of parents about oral health and care of preschool children in the central region of Saudi Arabia was deficient. Gender, education level of parents, family income, attitude about teeth and self-perception of parents‟ own dental health were factors which influenced their knowledge. To improve parents‟ knowledge, role of health professionals should be improved and TV commercials directed toward the areas which had gaps in knowledge.

Based on the teaching material of "Robotics" course, this paper studied the automatic knowledge graph construction, including knowledge points extraction and knowledge point relation extraction. We proposed a new method of extracting the first-level, second-level, and third-level knowledge points as well as their prerequisite relations of knowledge points in the textbook. For the problem of insufficient knowledge of the pre-trained language model in the specific field, methods such as incremental pre-training and optimization of cost functions are employed to integrate subject knowledge into the pre-trained language model, thus improving its effectiveness. To overcome the problem that the traditional method of relationship extraction can not be applied directly to the extraction of teaching materials, a new scheme for knowledge point relationship extraction based on keyword relationship is proposed. The experimental data from textbooks shows that the F1 score of knowledge point extraction reaches 93%, considerably improved compared to the traditional model. Consequently, the knowledge point entity extraction and relationship extraction methods based on the pre-trained model can effectively extract structured information and facilitate the automatic construction of knowledge graphs.

This study explores the implementation of an interdisciplinary curriculum in product and media design education and its impact on knowledge innovation and competency development. The curriculum is based on scaffolding theory, incorporating design proposals, workshops, digital design, marketing tests, and marketing activities guided by teachers and mentors from a branded ceramic workshop. The research findings demonstrate that the interdisciplinary curriculum facilitates knowledge innovation and fosters the development of students' professional skills, creativity, and problem-solving skills. The study also highlights the importance of stable scaffolding, including artefact, peer, and teacher support, which significantly contributes to cultivating transformational competencies, as outlined in the 2030 OECD Learning Compass. Additionally, the collaboration with Shanshing Four Seasons Celadon Studio on a patented ceramic product, "Funny Monkey," is a tangible example of the journey towards a triple-helix knowledge economy. This research underscores the importance of interdisciplinary curricula in promoting knowledge innovation and integrating transformational competencies in education. Scaffolding theory provides practical guidance for student learning and teaching strategies, presenting a sustainable roadmap for developing interdisciplinary curricula and offering a concrete and transferable pedagogical prototype for educational innovation.

Knowledge processing is an important feature of intelligence in general and artificial intelligence in particular. To develop computing systems working with knowledge, it is necessary to elaborate means of working with knowledge representations (as opposed to data) because knowledge is an abstract structure. There are different forms of knowledge representations derived from data. One of the basic forms is called a schema. The goal of this paper is the development of theoretical and practical tools for processing schemas. To achieve this goal, we use schema representations elaborated in the mathematical theory of schemas and use structural machine as a powerful theoretical tool for modeling parallel and concurrent computational processes. We describe the schema of autopoietic machines as physical realizations of structural machines. An autopoietic Machine is a technical system capable of regenerating, reproducing and maintaining itself by production, transformation and destruction of its components and the networks of processes downstream contained in them. We present the theory and practice of designing and implementing autopoietic machines as information processing structures integrating both symbolic computing and neural networks. Autopoietic machines use knowledge structures containing the behavioral evolution of the system and its interactions with the environment to maintain stability by counteracting fluctuations.

Water scarcity presents one of the greatest challenges of our time. Especially in naturally water scarce regions, the need of additional water resources is rising, requiring innovative and site-adapted technologies. In this review, selected non-conventional water technologies for sustainable water management are strategically evaluated. Focused on a matrix-based assessment incorporating key indicators, the study discusses the availability, applicability, environmental impact, scalability, and economic viability of the selected technologies. Based on a comprehensive literature review, the methodology involves a systematic comparison of technologies for desalination, water reuse, groundwater utilization, agricultural reuse, and unconventional approaches like cloud seeding, dew water, and fog water harvesting. The indicators covering the respective categories are presented in the results. On this basis, the different technologies are analyzed by matrix-based evaluation, highlighting various strengths and weaknesses and providing insights into technology selection based on regional needs. The discussion interprets the findings, deriving implications for dry environments, acknowledging limitations and suggesting pathways for future research. Through a rigorous analytical framework, this study contributes to the discourse on sustainable water solutions, offering valuable insights for policymakers, researchers and industries during a decision-making progress.

This paper investigates, experimentally and numerically, the buckling behavior of innovative sustainable Low-Cost Bamboo Composite (LCBC) structural columns under compressive loading. The LCBC columns are manufactured from bamboo culms in combination with bio-based resins to form composite structural columns. Different LCBC cross-sectional configurations are investigated in this study including the Russian doll (RD), Big Russian doll (BRD), Hawser (HAW), and Scrimber (SCR). Extra-large, large, medium, and small sizes of bamboo are employed to form the proposed configurations. Two bio-based resins including one bio-epoxy and one furan-based resin, in addition to a soft bio-based filler and a synthetic epoxy resin are applied. The bamboo species used as the cast-in-place giant bamboo for all configurations include Moso, Guadua, and Tali. In addition to experimental testing of the slender LCBC short columns, finite element analysis (FEA) software ABAQUS is applied to model the composite columns and predict their behavior. A buckling analysis using static Riks method is carried out to determine the LBCB’s response to axial compressive loading. A model is calibrated using test results with various imperfections, accounting for the variable behavior of each bamboo specimen, to accurately predict the behavior of LCBC columns with different resin contents. Slender LCBC columns showed maximum stress at buckling up to 60 MPa, highlighting the potential of bio-based resins for structural applications. The study found that the samples with bio-epoxy resin (BE2) exhibited enhanced material stiffness when compared to with synthetic epoxy (EPX) and furan-based resin (PF1), while PF1 specimens demonstrated increased ductility. The BE2 samples showed strain hardening in their stress-strain behavior, and the PF1 samples exhibited a long plateau phase before reaching the maximum load. Among the specimens with Moso bamboo and BE2 resin, those with SCR and HAW configurations achieved the highest compressive strengths.

Frailty is a common condition in older adults that negatively impacts health and quality of life. This study evaluated a comprehensive, personalized, and coordinated intervention under the Value-Based Care approach to address frailty's multidimensional nature in older people. It em-ployed a pre-post randomized controlled design, involving frail individuals over 65 years in Valencia, Spain. Assessments were conducted at baseline, 12 months (immediately post-intervention), and 18 months. The intervention included a personalized care plan supported by technology, with monthly motivational follow-ups and plan updates by health professionals and participants. Outcomes were measured using an assessment questionnaire that included the International Consortium for Health Outcomes Measurement dataset for the older population: physical health, physical functioning, general mental health, satisfaction with social activities and relationships, ability to carry out usual social roles and activities, pain, general quality of life, loneliness, physical frailty, psychological frailty and social frailty. The study found signifi-cant improvements in physical frailty, quality of life, reduced health resource use and hospitali-zations, and lower levels of pain and depression/anxiety compared to baseline. The findings suggest further research into value-based care approaches, emphasizing the development and activation of personalized, comprehensive programs for older individuals with frailty.

Physiologically Based Pharmacokinetic Models (PBPK) are mechanistical tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognised by regulatory authorities for predicting organ concentration-time profile, pharmacokinetic and daily intake dose of xenobiotics. Extension of PBPK models to capture sensitive populations like pediatric, geriatric, pregnant females, fetus etc. and diseased population like renal impairment, liver cirrhosis etc. is a must. However, the current modelling practice and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology, and calculation of biochemical parameters for integrating the knowledge and refining existing PBPK models. Specific PBPK covering compartments like cerebrospinal fluid, and hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints like developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in-silico models where experimental data is unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building qAOPs, use of machine learning for improving existing models along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

The emotional dimension in education has become increasingly important in recent decades. Enhancing the emotional dimension of prospective teachers in science subjects is higher education stuff responsibility. The implementation of active methodologies could modify the traditional student-teacher roles that are encouraged by the educational policies implemented in the Bologna Process. The principal aim of this work is to describe a Project Based Learning methodology and to introduce it as potential resource for the emotional and cognitive improvement of 19 prospective primary teachers enrolled in a scientific subject. This is a qualitative study with a transversal sustainability approach in the context of a research line focused on Higher Education for Sustainable Development. A questionnaire was designed and filled by the students at two different times, before and after implementation of the activity. The initial feedback from students was surprisingly enthusiastic by the fact that they were working with rockets, despite of this is not a common emotion in the science field. The results show the emotional improvement of prospective teachers after the implementation. It is concluded that a correct science education is necessary during the training of teachers taking into account their emotional dimension and the social repercussion due to the future transmission.

Cellular agriculture is defined as the production of agricultural products from cell cultures rather than from whole plants or animals. With growing interest in cellular agriculture as a means to address the public health, environmental, and animal welfare challenges of animal agriculture, the concept of producing seafood from fish cell- and tissue-cultures is emerging as a means to address similar challenges with industrial aquaculture systems and marine capture. Cell-based seafood - as opposed to animal-based seafood - can combine developments in biomedical engineering with modern aquaculture techniques. Biomedical engineering developments such as closed-system bioreactor production of land animal cells create a basis for large scale production of marine animal cells. Aquaculture techniques such as genetic modification and closed system aquaculture have achieved marked gains in production that can pave the way for innovations in cell-based seafood production. Here, we present the current state of innovation relevant to the development of cell-based seafood across multiple species as well as specific opportunities and challenges that exist for advancing this science. The authors find that the physiological properties of fish cell- and tissue- culture may be uniquely suited to cultivation in vitro. These physiological properties, including hypoxia tolerance, high buffering capacity, and low-temperature growth conditions, make marine cell culture an attractive opportunity for scale production of cell-based seafood; perhaps even more so than mammalian and avian cell cultures for cell-based meats. This, coupled with the unique capabilities of crustacean tissue-friendly scaffolding such as chitosan, a common seafood waste product and mushroom derivative, presents great promise for cell-based seafood production via bioreactor cultivation. To become fully realized, cell-based seafood research will require more understanding of fish muscle culture and cultivation; more investigation into serum-free media formulations optimized for fish cell culture; and bioreactor designs tuned to the needs of fish cells for large scale production.

When an aircraft malfunctions, quickly and accurately identifying the faulty unit is essential for ensuring normal operation. Unfortunately, maintenance engineers often struggle to acquire the necessary fault-related knowledge due to poor management and utilization of aircraft fault documents. To address this issue, we introduce knowledge graph technology into the field of aircraft fault diagnosis, exploring its construction and application for effective knowledge management. Our work starts by analyzing the critical knowledge elements required for aircraft fault diagnosis and designing a schema layer for fault knowledge graphs. We then we then combine deep learning and heuristic rules to extract fault knowledge from both structured and unstructured data, enabling the construction of aircraft fault knowledge graphs. Finally, we develop a fault question-answering system based on fault knowledge graphs that can accurately give solutions to questions posed by maintenance engineers. Our practice demonstrates that knowledge graphs provide an effective means of managing aircraft fault knowledge, assisting engineers in locating fault reasons accurately and quickly.

A claim management office (CMO) is a new intra-organisation fit based on an organisational project management (OPM) approach and deals specifically with improving claim performance. However, CMO is either misunderstood or often overlooked in construction companies, mainly due to ignorance of the OPM context but further exacerbated by a lack of real case study research on how to adopt CMO in these organisations. This study addresses this prevailing knowledge gap by investigating the nature of the construction industry from the CMO viewpoint and developing an extensive framework through which to improve organisational claims performance. Organisational ambidexterity theory integrated with X-inefficiency theory is adopted for claims performance outcomes, due to organisation intra-firm irrational decisions when managing such claims. Data was obtained and analysed from a international large construction company (which used CMOs) and its internal focus group discussion (as a multi-method approach). The reference framework provides new perspectives on how construction-related companies can adopt a CMO structure, which enables them to improve claims performance by planning in three interrelated activities viz. function-, process- and performance-based-. Furthermore, findings contribute to researchers and practitioners by providing a true understanding of the CMO-related mechanism and plausible roadmap for future work.

This paper presents a case study of Fulani herdsmen in Nigeria, whose traditional ethnoveteri-nary practices risk being lost as the country transitions to more intensive and enclosed livestock practices. We use a planetary health framing to make visible the value of indigenous practices that are less damaging to the environment, animal welfare and human health. Through ethnographic observation, focus group discussions (FGD) and key stakeholder interviews, we show that the Fulani use a complex system of herbal medicines and traditional herding practices to maintain herd health, manage and treat animal disease when it arises. However, their traditions often sit uncomfortably with commercial farming practices. As traditional Fulani grazing lands are eroded, dispossessed Fulani take employment from businessmen farmers but both parties’ inex-perience with shed hygiene, artificial feed and less environmentally resilient crossbreeds leads to increased incidence of infectious disease. This in turn drives higher use of antibiotics. There is a ‘causal chain’ of underlying drivers that leads, through poorer environmental, animal and hu-man health, to increased use of antibiotics. The antibiotic resistance that emerges from this chain threatens human health now and into the future. Through a planetary health framing we advocate for more respect for Fulani herdsmen and their traditional ethnoveterinary practices as an alter-native to increasing antibiotic use.

Background: Corticosteroids (CS) are crucial for managing various medical conditions, but long-term use is associated with significant risks. Balancing the therapeutic benefits and potential hazards requires careful prescription and monitoring. This study explores Jordanian physicians' knowledge, experiences, and concerns regarding CS prescriptions. Methods: This was a cross-sectional study conducted between March and August 2023, using an online link distributed using social media platforms. Results: A total of 171 physicians enrolled in the study were predominantly male, with a median age of 31 years. Two-thirds worked in central Jordan, and one-third were general practitioners. The majority (86.5%) had experience both in topical (83.1%) and injectable (81.1%) dosage forms. Physicians demonstrated high knowledge (median score 11/11) and major concerns (median fear score 3.5/5) about CS. Major worries included the risk of osteoporosis (73.7%) and hyperglycemia (66.7%). Sociodemographic factors did not significantly impact their knowledge or fears. Conclusion: Our study showed that physicians have a high knowledge level about CSs but have major concerns about CS side effects, particularly osteoporosis and hyperglycemia. These findings highlight the need for targeted interventions to address these fears, enhancing physician confidence and improving patient management outcomes.

Keywords: Knowledge; Attitudes; Practices; Salt intake.

Inconsistent results published in previous studies make it difficult to determine the precise effect of consumer knowledge on their acceptance of functional foods, which were developed to improve consumers’ health status by providing adequate nutrition. We conducted a systematic review and meta-analysis by identifying and collecting relevant literature from three databases. Of the 1050 studies we reviewed, we included 40 in the systematic review and 18 in meta-analysis. Based on the focus of each included study, we operationally defined knowledge as knowledge of the functional food concept, nutritional-related knowledge, and knowledge of specific functional products. Results from the systematic review indicate that most participants from the included studies had a low level of knowledge, especially nutrition-related knowledge associated with consuming functional foods, and they were generally not familiar with the concept of functional foods. It is possible that participants’ level of knowledge was influenced by their demographic characteristics (e.g., age, gender, educational level, marital status, nationality). Results from the meta-analysis generated a summary effect size (r = 0.14, 95% CI [0.05; 0.23]), measured by the correlation coefficient r, which indicates that a small positive relationship exists between consumers’ level of functional foods knowledge and their acceptance of functional foods.

Water literacy is the ability to feel familiar with and get actively involved in water to face issues about it. This study aims to analyze the water literacy of students who come from areas that have indigenous knowledge related to water conservation and do not have indigenous knowledge but have come from the same province with a similar abundance of water. The samples of this study were 439 people consisting of 184 boys and 255 girls aged 11-18 years from junior and senior high school students in Palembang Municipality and three sub-districts in Muara Enim Regency, namely Semende Darat Laut, Semende Darat Tengah, and Darat Ulu and both are indonesian. There were 39 questions for measuring water literacy in the form of a Likert scale (1-5) to measure practical and living literacy with Cronbach alpha values of 0.692 and 0.773, respectively, and were analyzed with SPSS version 21 through the Mann-Whitney (u) and Cronbach alpha statistical tests. The total alpha was 0.838. Social literacy was measured in the open-ended question instrument and analyzed descriptively. The results showed that practical water literacy in Palembang was significantly different from Semende's but not with living water literacy. Students' social water literacy in the Semende had two different answer patterns, while, in Palembang, it was more diverse with six different response patterns. Meanwhile, when asked to describe a diagram of the flow of water in nature in Semende, no students think that it is the neglect of customs that results in reduced water in Semende as understood by some students in Palembang who are not involved with these customs.