Traditional Medicine (TM) is a culturally-rooted healthcare practice, and its use, including for treating illnesses among children, has been reported in many countries. Despite the cultural significance of TM in managing children’s illnesses, evidence suggests that child mortality remains high in many settings. This indicates that the sole use of TM within families and communities might act as a barrier to using biomedical services, contributing to avoidable deaths. Globally, Indonesia is in the top 10 countries with the highest child mortality rates. In addition, Belu is a district in East Nusa Tenggara province, Indonesia that exhibits a high number of child deaths within the last three years. However, the use of TM for the treatment of illnesses among children in Indonesia has received minimal academic attention. This study aimed to understand parental beliefs, knowledge, views, and perceptions of TM and the impact on the use of TM and biomedical services in the treatment of illnesses among their children in Belu district, Indonesia. The participants (n=25) were parents with the real-life experiences of using TM for treating their children’s illnesses. They were recruited using the snowballing sampling technique. Data were collected using in-depth qualitative interviews. A thematic data analysis was performed and guided by a qualitative data analysis framework. The findings were grouped into four main themes: (i) parental beliefs, knowledge, views, and perceptions of TM, (ii) the influence of these factors on parental use of TM and biomedical services for the treatment of children’s illnesses, (iii) social and cultural influences on the use of TM and biomedical services among their children, and (iv) the impact of the sole use of TM or late medical diagnosis on the health condition and the death of children. The findings show that the use of TM is a common practice within families and communities in Belu, however evidence suggests that child mortality remains high in the setting, indicating less effectiveness of the sole use of TM. Thus, there is a need for the development of collaborative models of healthcare programs and service delivery fostering mutual respect and understanding between traditional healers and healthcare professionals, which could significantly contribute to improving parental use of biomedical services for the treatment of their children’s illnesses.

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.

Indigenous traditional land uses, including hunting, fishing, sacred activies and land-based education at Red Sucker Lake First Nation (RSLFN) in Manitoba, Canada are impacted by mining. Traditional land use maps and interviews were undertaken with 21 Indigenous people from RSLFN, showing many traditional land uses are concentrated on greenstone belts. The interviews revealed that mining exploration has resulted in large petroleum spills, noise distress, personal property destruction, wildlife die-offs and animal population declines, which negatively impact RSLFN’s traditional land use practices, ecosystem integrity, and community health. Red Sucker Lake First Nation (RSLFN) people want their territories’ land and water protected for traditional uses, culture and ecological integrity. Towards this goal, their Island Lake Tribal Council sought support for an Indigenous-protected and conserved area (IPCA) in their territory outside of existing mining claims, but without success. Governments need to partner with Indigenous nations to reach their biodiversity targets, particularly considering northern Canada’s peatlands, including those in Island Lake, surpassing the Amazon forests for carbon storage. Critical minerals and gold’s role in renewable energy and geopolitics have colonial governments undermining Indigenous rights, climate stabilization and biodiversity. With extractivism prioritized, the environmental impacts of mining extend to not only the mines but also the extensive development required to facilitate extraction including roads, hydro and ports to ship the minerals with proposals for a national Northern Corridor to run nearby.

The purpose of this paper is to propose a novel transfer learning regularization method based on knowledge distillation. Recently, transfer learning methods have been used in various fields. However, the problems such as knowledge loss still occur during the process of transfer learning to a new target dataset. To solve these problems, there are various regularization methods based on knowledge distillation techniques. In this paper, we propose a transfer learning regularization method based on feature map alignment used in the field of knowledge distillation. The proposed method is composed of two attention based submodules: self-pixel attention (SPA) and global channel attention (GCA). The self-pixel attention submodule utilizes both the feature maps of the source and target models, so that it provides an opportunity to jointly consider the features of the target and the knowledge of the source. The global channel attention submodule determines the importance of channels through all layers, unlike the existing methods that calculate these only within a single layer. Accordingly, transfer learning regularization is performed by considering both the internal of each single layer and the depth of the entire layer. Consequently, the proposed method using both of these submodules showed overall improved classification accuracy than the existing methods in classification experiments on commonly used dataset.

Tribal healers and local practitioners possess knowledge about plant uses passed down through generations. However, habitat loss, unsustainable harvesting, and declining traditional knowledge threaten this valuable resource. India, a global biodiversity hotspot, is rich in medicinal plants. Documenting the traditional knowledge prevents it from being lost and ensures the preservation of this cultural heritage. Recording traditional practices helps bridge the gap between traditional and modern medicine. This study quantifies the ethnomedicinal uses of plants within the Sohag Wildlife Sanctuary. The findings contribute to preserving traditional knowledge and promoting the sustainable use of medicinal plants. The study documented 95 medicinal plant species from 39 families, highlighting the importance of ethnomedicinal studies for preserving traditional knowledge and ensuring the sustainable use of medicinal plants. In the future, scientific studies can be organized to understand their efficacy, leading to developing new, potentially safer, and more effective medications. Identifying and documenting medicinal plants is crucial in safeguarding cultural heritage, fostering scientific discovery, and promoting sustainable healthcare practices.

Strategic intelligence covers the examination of what impacts and influences the business and the related SWOT analysis, (strengths, weaknesses, opportunities, and threats mapping). Through this research, leaders will be able to properly allocate and re-route resources, identify areas of improvement, and ensure that strategic initiatives are aligned with the organization's mission. Strategic intelligence is, therefore, effective through the use of critical thought and data-driven decisions and seeing the outcomes of different positioned alternatives. It must also form and support a culture of cooperation and open communication within the organizations, stimulating employees' inputs and learning curves. The article's subject is the necessity for strategic intelligence, a crucial competence for leaders, managers, and companies striving to remain competitive, to be utilized and best leveraged in today's volatile and dynamic business contexts. It helps the leadership turn proactive, foresee trends, and make correct, agile decisions that enforce companies' long-term growth and success. This study follows a research design that includes a thorough literature review and bibliometric research, executed via VoSViewer, illustrating the concept of strategic intelligence from a leadership point of view as a driver of competitive advantage. Further, the article points out that the limitations are acknowledged because the literature on the matter is limited, as strategic intelligence is relatively new to the business sector; moreover, more research is required to fully understand and use the potential of strategic intelligence in business growth. By overcoming these limitations and continuing to study this concept, businesses can gain a competitive edge and make informed decisions that fuel their success in an ever-evolving market. As a result, this study contributes to developing strategic intelligence in leadership and business and paves the road for further research and applications of strategic intelligence theories in organizational contexts.

Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of the Driving Scenario Knowledge Graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable Driving Scenario Knowledge Graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.

The review addresses a critical research gap in ethnobotanical knowledge within Kerala's tribal communities. A scarcity of studies hampers our understanding of their traditional practices, risking cultural erosion and knowledge loss. Synthesizing existing documentation, it aims to provide a comprehensive overview, emphasizing the urgent need for further research to impact sustainable resource management, biodiversity conservation, and tribal empowerment.

With the progression of AI, embedding advanced AI technologies into small robotics and mobile devices has become essential, driving research towards lightweighting AI models. Our study enhances the EPINET depth estimation model for light field images, aiming for compactness and faster inference while preserving accuracy. We conducted two-step experiments aimed at enhancing inference efficiency: Initially, by adjusting input streams and convolution layers, we simplified the CNN model, achieving faster inference times at the cost of reduced accuracy. To address this reduction in accuracy, we then applied knowledge distillation, allowing the simplified model to learn from the original model’s more complex patterns. In our quantitative experiments using two error metrics, MSE (Mean Squared Error) and BadPix, we identified optimal knowledge positions and evaluated the required complexity for the student model. As a result, our method improved MSE by 21% and BadPix by 14% compared to training without it. Furthermore, the student model achieved an inference speed 13% faster than the teacher model and surpassed its accuracy by 10% in MSE. Additionally, we demonstrated that repeatedly applying our approach could further enhance both model compactness and accuracy.

Knowledge is not merely the characteristic of certain pieces of information being deemed true and justifiable, but rather a process. Within an axiomatic system, the set of information that reaches the status of knowledge forms a universe that provides an interpretation of the reality one aims to describe. Language exchanges information through communication and the process of justification (exchange of information), if it consistently justifies a particular piece of information, it elevates it to knowledge. In this paper, we provide a framework for knowledge within an axiomatic model where we define the fundamental elements of communication and grammar. As a result, the structure dictates that knowledge, to be considered as such, must be investigateable by anyone who utilizes it in its entirety, both in form and content. Ultimately, the development of knowledge processing, referred to as \emph{knowmatics}, represents a clear evolution of the computer science that processes information (subset of knowledge).

Supply chain disruptions, a pandemic, and war in Ukraine have exposed faultlines in a globalised food system that depends on a few staple crops grown in a few exporting regions and transported to consumers around the world. In the UK, just three crops, (wheat, barley, and oilseed rape), account for 75 per cent of the UK’s 4.5 million hectares of arable land whilst the country imports around half its food - nearly 40 per cent from just four EU countries (the Netherlands, Ireland, Germany, and France). Poor diets contribute to one in seven deaths in the UK, 63 per cent of the population is overweight or obese and health inequality is increasing between the poorest and most affluent regions. The food security and health of the UK population is therefore dependent on a small number of locally grown crops, vulnerable supply chains, and an unhealthy, obesogenic diet. The UK food system must diversify if it is to become food and nutritionally secure, meet its climate and biodiversity goals and have a healthy and active population. Climate resilient and nutritious underutilised crops can help diversify the UK agrifood system but, research and investment in them is sporadic, piecemeal, and unfocused. In this paper we compare two approaches to identifying potentially suitable underutilised crops for the UK. The first, based on Defra Project CH0224 was delivered through literature and database searches and expertise of growers, advisers, breeders, seed suppliers, processors, traders, and researchers. The second, used the CropBASE digital knowledge base for underutilised crops described in Jahanshiri et. al. (2023). The two approaches produced no single crop that was common to both shortlists. We propose that the analytical and predictive tools derived from CropBASE could be combined with local knowledge and expertise from the Defra project to provide a common framework for the identification of underutilised crops that are best suited to local UK circumstances now and in climates of the future.

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.

This study aimed to investigate the knowledge of patients with hypertension about their condition, adherence to antihypertensive medication, and the factors influencing it. A cross-sectional study was conducted in two cardiology outpatient clinics of two tertiary hospitals, in Greece. The study included 188 patients diagnosed with hypertension. The patient’s knowledge about their disease and adherence to medication were assessed by using the HK-LS and A-14 scales, respectively. Patients had sufficient knowledge levels about their disease, but significantly low levels of adherence to medication. Patients with higher knowledge levels were more adherent to medications [r(188) = 0.885, p < 0.001]. By using multivariate analysis, higher age (p = 0.018), residence in a more populous area (p = 0.041), more years with the disease (p = 0.012), and a lower number of medications (p = 0.03) were associated with higher levels of knowledge. Conversely, younger age (p = 0.036), lower educational levels (p = 0.048), fewer years with the disease (p = 0.001), and a higher number of medications (p = 0.003) were associated with lower adherence to medication. The Greek patients' hypertension knowledge was sufficient, however, adherence to medication was significantly low. Healthcare managers could utilize our findings to design targeted interventions for improving adherence to medication for these patients.

Background: Dermatophytosis, commonly known as tinea, poses a significant public health concern worldwide, especially in environments with poor hygiene and overcrowding, such as prisons. Despite its prevalence and impact on quality of life, there is a lack of research on the knowledge and hygiene practices regarding dermatophytosis among prisoners, particularly in Nepal. Objective: The study aimed to assess prisoners' knowledge, hygiene practice and infection status regarding dermatophytosis in Central Prison, Nepal. Methods: A descriptive cross-sectional study with a sample size of 184 respondents was designed to collect data using a validated pre-tested questionnaire from September 2023 to January 2024. The collected data was then analysed using IBM SPSS version 21. Knowledge and hygiene practices were measured on an eight and 11-point scale and rated as poor (≤4) and sound (>4), bad (≤6), and good (>6), respectively. Summary data were presented by descriptive, while Chi-square and logistic regression were used for inferential statistics at P < 0.05. Results: The findings revealed moderate knowledge among prisoners regarding dermatophytosis, with significant gaps in understanding its spread and prevention. While most prisoners recognised the importance of treatment, there were misconceptions about the inevitability of contracting dermatophytosis and the role of personal hygiene. Conclusions: Despite good knowledge levels, adherence to recommended hygiene practices was suboptimal, highlighting the need for targeted interventions. The study underscores the importance of addressing knowledge gaps, changing attitudes, and promoting hygienic practices to mitigate the burden of dermatophytosis among prisoners.

(1) In an era where sustainable behavior is increasingly crucial, understanding the discrepancy between individuals' sustainability-oriented values and their actual behaviors, known as the inner-outer gap, is vital. This study explores the potential of the Tripartite Structure of Sustainability (TSS) framework to address this gap within the context of sustainable education. By reviewing literature from the databases APA, ERIC, and Web of Science, we systematically examined factors influencing sustainable actions. (2) Employing a qualitative content analysis approach, we analyzed 56 articles over a 15-year period, identifying 83 factors that either bridge or reinforce the inner-outer gap. These factors were categorized within the TSS framework, which segments sustainability-oriented actions into individual, social, and self-transcendent domains, alongside their activation points: stable, situational, or automated. (3) Our analysis revealed that self-focused factors often reinforce the gap, suggesting a hindrance to sustainable behavior. Conversely, self-transcendent factors consistently bridged the gap, promoting sustainability. Social factors showed variable impacts based on the ethical and sustainable context they were placed in, suggesting that the social environment's orientation significantly influences sustainable behavior. (4) The study concludes that the TSS framework offers a promising approach to advancing sustainability in education by facilitating a paradigm shift towards holistic and interconnected perspectives. This shift is critical for fostering sustainable behaviors, thereby contributing to the field of educational psychology and offering practical implications for educators aiming to nurture a sustainable future.

To assess the community health workers’ (CHWs) knowledge, attitude, and practices (KAP) in type-2 diabetes (T2D) and hypertension (HTN) management. We thus also sought to assess the potential health system barriers for incorporating CHWs in T2D and HTN management. Mixed-method study comprised (1) a survey among 153 active CHWs to assess their KAP of T2D and HTN, and (2) semi-structured interviews with key informants to understand challenges of the health system and proposed solutions in incorporating CHWs in T2D and HTN management. The majority of CHWs correctly answered general knowledge questions on NCDs, risk factors, and prevention; however, only one-quarter correctly answered questions on smoking, family history, or tobacco use as risk factors. Most respondents appeared to have positive attitudes and have been practicing some activities related to T2D and HTN. Both financial and non-financial resource constraints were cited as challenges of the health system; therefore, re-structuring the role definition of CHWs and task of responsibilities, and assessment the need and workload have been proposed as ways forwards to effectively incorporate CHWs in T2D and HTN care. CHWs in Cambodia have shown their potential in T2D and HTN management; however, a well-designed strategy including technical training, clearly defined roles and responsibilities, and strong supporting structure are important to maximize their potential in the health system.

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.

Artificial intelligence (AI) has witnessed an exponential increase in its use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software engineering problems. However, a comprehensive and holistic understanding of the current status is missing. To close the above gap, synthetic knowledge synthesis was used to induce a research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and five themes, namely natural language processing in software engineering, use of artificial intelligence in the management of software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n=2042), followed by the United States (n=1193), India (n=934), Germany (n=445), and Canada (n=381). A high percentage (n=47.4%) of papers were funded, showing a strong interest in this research topic. The convergence of AI and software engineering can significantly reduce needed resources, improve quality, increase user experience, and improve the well-being of software developers.

The assessment of existing bridges constitutes a serious challenge for the researchers since their safety evaluation is a complex task due to several sources of uncertainty affecting the parameters identified in the knowledge process. In particular, a high level of uncertainties may arise in the case of prestressing systems requiring special investigation strategies and experimental technologies. The paper aims to investigate how the knowledge uncertainties influence the safety assessment of existing post-tensioned bridges. The semi-probabilistic approach is used, and uncertainties related to the knowledge process are described considering the number of tests, the confidence level assumed for the estimation of the problem parameters, and the measurement errors of in-situ tests. Results concerning a typical post-tensioned bridge are reported and the variability of the outcomes is discussed and compared with a reference case of “perfect knowledge”. The main results of the study confirm the general robustness of the semi-probabilistic Eurocodes safety format and provide an overview of dispersion due to different choices about the number of tests and confidence level. The measurement errors may lead to significant under-estimation of the computed structural capacity with respect to the reference one.

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model that is often referenced in both academics and practitioner circles will be discussed and shown to be faulty on several levels, including. a lack of definitional agreement. A new DIKW framework will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move to Intelligent Digital Twins (IDT) with Artificial Intelligence (AI).

Sexual Violence (SV) can deeply impact victims' physical and psychosocial well-being. Yet many healthcare providers, including registered nurses (RNs), hesitate to screen patients due to a lack of confidence and knowledge. The SATELLITE Sexual Violence Assessment and Care Guide was developed to address this gap; however, the guide’s educational effectiveness remained untested. This pilot study aimed to assess the feasibility, acceptability, and efficacy of an education program based on the SATELLITE guide among RNs in clinical settings, using a pre- and post-test design. Results indicated that the education was not only feasible and acceptable, but also demonstrated the effects as desired with significant increases in RNs' knowledge and confidence in SV screening and care. The program's assessment tool was valid and reliable, and participant recruitment was feasible. Based on these findings, it is recommended that the SATELLITE education program be further tested with a larger RN sample and extended to other healthcare providers. Additionally, exploring different regions, cultural contexts, and healthcare settings would enhance understanding of the program's broader applicability and effectiveness.

In the dynamic interplay between people and their physical environments, the Global South stands as a mosaic undergoing a multitude of transformative influences in architecture and urbanism where examining social sustainability becomes imperative. While prevailing attention remains on environmental and economic sustainability, this study addresses a persistent gap in the urban literature by focusing on the dynamic and manifold nature of social sustainability. Positioning itself within the context of sustainable development, the study links the pursuit of social aspects of sustainability with selected unique urban contexts from the Global South. Five cases that include Alexandria (Egypt), Tripoli (Libya), Basra (Iraq), Lilongwe (Malawi), and Accra (Ghana) are discussed through multi-layered investigations that involve attitude surveys, interviews, focus groups, participatory systematic observations, and behavioral mapping, engaging directly with inhabitants and stakeholders. Uncovering people-place narratives in the identified contexts, the cases are developed into five knowledge typologies that serve as practical tools for planning and design decision-making, policy formulation, and academic discourse. Discussions are conceived to demonstrate the transformative role people-place narratives play in fostering a more sustainable and equitable urban future. Conclusions are drawn to offer practical insights for stakeholders involved in various capacities in shaping the urban landscape of the Global South.

We develop new theory from a broad case-study approach to explore a better understanding of what constitutes knowledge and its value as identifiable to human-machine teams. From past research, this trail of exploration will lead to these initial questions: What is the value of debate in the furtherance of knowledge? Will machines with AI be able to contribute to a debate if we humans cannot define it or determine its value sufficiently for a machine’s understanding, contribution, exploration and identification? Like humans, as teammates, machines must be able to determine with AI what constitutes the usable knowledge that contributes to a team’s success in the field (e.g., testing "knowledge" in the field, identifying new knowledge, using knowledge to develop innovation) or its failure (viz., trouble shooting; identifying weaknesses; discovering vulnerabilities, hiding by deception). It matters not whether a debate is public, private or unexpressed by an individual human or machine agent alone; we speculate in this exploration that the process advances the science of autonomous human-machine teams and assists in interpretable machine learning. We conclude with questions and a speculation: How does a human become aware or express its awareness of knowledge? Can a machine be as expressive as its human teammates? And how does a human-machine teammate become aware that its teammates possess sufficient knowledge to perform a task? We speculate that the structure of “knowledge," once found, is resistant to alternatives (i.e., it is ordered); that its functional utility is generalizable; and that its applications are multifaceted (akin to maximum entropy production). The complexity of the team is taken into consideration in our search for knowledge, which can also be used as an information metric.

Type 2 diabetes (T2DM) presents an enormous global healthcare challenge, especially among rural communities due to limited access. To overcome this, many low- and middle-income countries are resorting to task shifting, using community health workers (CHWs) for diabetes management. However, its successful implementation depends on the practice behaviours and knowledge of these workers. This cross-sectional study from rural South India aimed to evaluate the proficiency of 275 CHWs in diabetes screening and management. A customized questionnaire, developed through experts and government was used to evaluate the practice behaviours and knowledge. Analytical methodologies consisted of descriptive statistics, logistic regression, and mosaic plots for comprehensive data interpretation. Results showed significant deficiencies of mean correct practice behaviours at 48 and 50% respectively. The identified gaps included diabetes diagnosis, HbA1c testing, diet, self-management, complications screening and risk assessment. In several areas, correct practice behaviour was reported by a relatively large number of CHWs despite incorrect answers to the related knowledge questions such as referral to the health centres, self-management and calculation of diabetes risk assessment. Addressing these deficiencies requires a thorough needs assessment and tailored training for improved diabetes management by CHWs. Training of CHWs, should not only identify prior knowledge and/or behaviour but also their interrelationship to help create a robust and flexible set of practice behaviours.

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.

Over the last two decades, knowledge production, research and reconfiguration of universities have been understood as ways of giving new meanings to the university-society binomial. In this regard, humanities are the subject of multiple debates in the face of ideas about their impact in relation to the "other sciences". Based on these premises, this article sets out to explore possible meanings attributed by researchers to the concepts of commitment, mobilization and transfer of research in humanities in view of the debates on the university-society interaction and the third mission of the university (Alonso, Cuschnir and Napoli, 2021). The methodology used will address bibliographical analysis, theoretical background and statements from different institutions, as well as the analysis of material from four interviews. As a first instance, the preliminary results show that strengthening critical thinking as forms of commitment emerge as central senses, focusing on hungarian characteristics and productions in order to unravel the ways of understanding and imagining Eastern European reality. In this respect, the discussion of certain western knowledge is seen as a task associated with social commitment with public universities as a focus of resistance.

To resolve problems in the dietary life of university athletes, education is essential for enabling athletes to change their own dietary behavior. The purpose of this research, it was decided to verify the effectiveness of sports nutrition education based on self-determination theory (SDT). Methods, the subjects were thirty-six male university rowers. A stratified randomized comparison test was conducted by student year (SDT group and control group). Sports nutrition education was held three times, via an Internet conferencing system. Furthermore, group work over social media was used for the SDT group. Four evaluations were carried out based on anthropometric measurements, brief-type self-administered diet history questionnaire (BDHQ),sports nutrition knowledge (SNK), and treatment self-regulation questionnaire (TSRQ). Results, for the intra-group factor "Protein," a significant difference was evident in the self-determination theory group (50.0±28.5, 78.6±28.1, 81.0±21.5, p<0.000, units: %), and there were significant differences between after and before, and between three months after and before (p=0.002, p=0.002). For BDHQ, significant differences were evident for green/yellow vegetables, fruits, and dairy between after and before for the self-determination theory group (159.1±74.2-126.7±70.6, p=0.009, 306.0±196.2-195.2±146.1, p=0.020, 257.0±147.0-183.3±167.9, p=0.040, units: g). In conclusion, sports nutrition education based on SDT improved dietary knowledge and increased dietary intake.

In this paper, we present findings from a qualitative interview study, which highlights the difficulties and challenges with quantifying carbon emissions, and discusses how to move productively through these challenges by drawing insights from studies of deep uncertainty. Our research study focuses on the digital sector and was governed by the research question: how do practitioners researching, working or immersed in the broad area of sustainable digitisation (researchers, industry, NGOs, and policy representatives) understand and engage with quantifying carbon? Our findings show how stakeholders struggled to measure carbon emissions across complex systems, the lack of standardisation to assist with this, and how these challenges led stakeholders to call for more data to address this uncertainty. We argue that calls for more data to address this uncertainty obscure the fact that there will always be uncertainty, and that we must learn to govern from within it.

Limited quantitative studies exist on wild edible plants of the Limpopo Province, South Africa. This has important implications for the livelihood sustainability of rural communities, retention of indigenous knowledge, domestication of such plants, and environmental conservation efforts. The current study focused on the use pattern of wild edible plant diversity from four municipalities in the Vhembe District Municipality. One hundred and sixty participants were interviewed to solicit information on the diversity of plants, growth forms, and their uses. Eighty-seven wild edible plants distributed amongst 34 plant families were documented. The botanical family Malvaceae was the most used, followed by the Cucurbitaceae. Plant parts such as the leaves and fruits were typically used. Due to seasonality, plant parts were normally preserved through a cook and dry, or collect and dry process. Flowers of other wild vegetables (viz. Cleome gynandra, Cucurbita pepo, and Sonchus asper) are edible. Noteworthy, the leaves of Morus alba var alba are used as a leafy vegetable. The results of this research will play a crucial role in the encouragement of continuous utilization of these plants and hence work towards shaping the creation of rural livelihood strategies. Furthermore, the outcomes of this research will enlighten the agricultural-reliant societies of the study area with the domestication of wild edible plants as a way of introducing new crop species.

Heat waves are a significant consequence of climate change threatening public health by increased morbidity and mortality. The aim of this study was to estimate individuals’ knowledge, attitudes and practice related to heat waves. We conducted an exploratory cross-sectional study in Greece during September 2023. We employed a convenience sample of 1055 participants. We used the heat wave knowledge, awareness, practice and behavior scale (HWKAPBS) to measure our outcome. We measured several socio-demographic variables, such as gender, age, educational level, etc. as potential determinants. Mean scores for the knowledge, awareness, practice and behavior factors were 12.5, 22.7, 22.2 and 12.1 respectively. Females had higher scores on the four factors compared with males. We found a positive relationship between self-perceived health status and awareness, practice and behavior about heat waves. Similarly, we identified a positive relationship between self-perceived financial status and awareness and behavior about heat waves. Increased age was associated with increased practice score, while increased educational level was associated with increased knowledge score. Additionally, behavior score was higher among participants in urban areas than those in rural areas. We found statistically significant positive correlations between the four factors. Levels of knowledge, awareness, practice and behavior about heat waves were high in our sample. Several socio-demographic variables affect participants’ knowledge, awareness, practice and behavior concerning heat waves.

This paper presents the second stage of a tandem fixed wing unmanned aerial vehicle (UAV) aerodynamic development. In the initial stage, the UAV was optimized analyzing its characteristics only in symmetrical flight conditions. Posted requirements were that both wings should produce relevant positive lift, the initial stall must occur on the front wing first, the center of pressure should be close to the center of gravity, while longitudinal static stability should be in the optimum range. Computational fluid dynamics (CFD) analyses were performed, where applied calculation model was derived from the authors’ previous successful projects. The eight version TW V8 has satisfied all longitudinal requirements. Lateral-directional CFD analyses of V8 showed that the ratio of the lateral and directional stability at the nominal cruising regime was optimal, but both lateral and directional static stabilities were too high. On further development versions the lower vertical tail was eliminated, negative dihedral was implemented on the front wing, and four inverted blended winglets were added. Version TW V14 has largely improved lateral and directional stability characteristics, while their optimum ratio at cruising regime was preserved. Longitudinal characteristics were also well preserved. Maximum lift coefficient and lift-to-drag ratio were increased, compared to the V8.

Background: Fertility is a crucial component of population maintenance and growth. A declin-ing fertility trend has been observed over the past two decades, and it continues. Objective: The aim of this research was to examine the level of fertility knowledge, factors in-fluencing fertility knowledge, and the relationship between fertility knowledge and the decision to have a child. Methods: A cross-sectional study was conducted on a sample of mothers in five hospitals on the Adriatic coast of the Republic of Croatia, involving 1541 mothers from September 2021 to De-cember 2023. The Cardiff Fertility Knowledge Scale was used for the research. Results: The participants were divided into two groups based on fertility status in women, spe-cifically the period of declining fertility in women. Group 1 included participants aged 18-31 with sustained fertility, and Group 2 included participants aged 32 and older experiencing de-clining fertility. Fertility knowledge was 58.9%. Women with higher educational status showed better results (62.3%). The most common sources of fertility knowledge were the healthcare sys-tem and the internet. Factors associated with the level of fertility knowledge included higher education, marital status, and higher economic status. Conclusion: The research results reveal a lack of fertility knowledge among participants, as well as an intention to have a child in later stages of life. The lack of formal education on this topic leads to information gathering from unreliable sources.

Background: Children's environmental health encompasses a wide range of factors that impact the well-being of children, including physical, chemical, biological, and social elements in their immediate environment. Safeguarding children from harmful substances is the crucial role of nursing students. Nursing students play a vital role as valuable resources for individuals, families, communities, and policymakers. This study aims to assess the knowledge and skills of nursing students concerning children's environmental health at academic institutions delivering nursing program in Nekemte town, Ethiopia. Methods: An institutional-based cross-sectional study design was carried out from August 1–30, 2023, among nursing students in Nekemte Town, and 634 study participants were selected through simple random sampling. Data was entered into Epinfo version 6.4 and exported to STATA version 14 for analysis, and multicollinearity was checked. Finally, from the bivariate analysis, variables of < 0.2 were included in the multivariate analysis; an adjusted odd ratio with a 95% CI was computed; and variables with a P-value <0.05 in the multivariable analysis were taken as significant associated factors. Results: The overall nursing students' level of knowledge about children's environmental health was insufficient (56%). More than one-third (34%) demonstrate their skill towards children's environmental health insufficiently, and one-fourth (25%) and 27% of the learners conduct their craft to prevent children from ecological hazards to a good and better level, respectively. While gender of the participant, age of the participant, year of the study, training history of the participants, and field of study of the students were significantly associated variables with their knowledge of children's environmental health. Conclusion: The study reveals that nursing students' knowledge of children's environmental health is limited, with factors such as gender, age, training history, and field of study significantly influencing their understanding. Therefore, female students should be considered for more tutorials to be effectively knowledgeable regarding children’s environmental health.

We assume that the current mathematical knowledge K is a finite set of statements from both formal and constructive mathematics, which is time-dependent and publicly available. Any theorem of any mathematician from past or present belongs to K. The set K exists only theoretically. Ignoring K and its subsets, sets exist formally in ZFC theory although their properties can be time-dependent (when they depend on K) or informal. In every branch of mathematics, the set of all knowable truths is the set of all theorems. This set exists independently of K. Algorithms always terminate. We explain the distinction between algorithms whose existence is provable in ZFC and constructively defined algorithms which are currently known. By using this distinction, we obtain non-trivial statements on decidable sets X⊆N that belong to constructive and informal mathematics and refer to the current mathematical knowledge on X. This and the next sentence justify the article title. For any empirical science, we can identify the current knowledge with that science because truths from the empirical sciences are not necessary truths but working models of truth about particular real phenomena.

This research paper examines the effects, advantages, challenges, and economic aspects of a research town. The study concludes that inclusion and diversity not only constitute fundamental values but also serve as crucial drivers of business success and sustained competitiveness. Universities play a central role in shaping the prosperity of the research town by functioning as knowledge hubs, fostering collaboration, nurturing talent, and contributing to the broader innovation ecosystem. The symbiotic relationship between universities and industries within the cluster establishes a dynamic and resilient environment for research and development. The presence of startups within the research cluster introduces novel ideas, enhancing the overall vibrancy and competitiveness of the local economy. In an era where social media algorithms increasingly influence people's choices, the potential for foreign interference emerges as a significant threat.

In Nigeria, Cervical cancer (CC) and lack of Human Papillomavirus (HPV) awareness poses significant HPV infection risk among women. This study was conducted at Ibrahim Badamasi Babangida (IBB) University in Nigeria; focused on assessing HPV awareness and knowledge, particularly concerning its association with CC and oral contraceptive (OC) pills among students and staff. We adapted and validated a 20-item HPV knowledge scale and explored factors influencing knowledge, vaccination rates, awareness, and the impact of ethnicity on HPV knowledge. Also, due to the non-normality of the knowledge score, non-parametric tests were done. Data were summarized using median and interquartile range (IQR), while categorical data were presented in frequency and percentage distribution. We established the reliability of the 20-item knowledge scale with a strong Cronbach's alpha coefficient of 0.913, indicating internal consistency. However, it revealed a concerning lack of awareness and knowledge about HPV, only 34.8% of participants were aware of HPV infection, and only 25.0% knew about HPV vaccine. Additionally, ethnicity was identified as a significant factor affecting HPV knowledge. Considering these findings, this study emphasizes the need for targeted interventions to enhance HPV awareness and knowledge, particularly among specific ethnic groups. Despite the development and validation of a reliable knowledge scale, the study underscores the importance of educational initiatives to address this knowledge gap, ultimately reducing the risk of HPV infection and CC in Nigeria.

Numerous studies have shown the multiple benefits of plant biodiversity and the crucial role of resident’s awareness in conservation and land management. Plant biodiversity is investigated in an analytical monitoring report conducted jointly with local population engaged in livestock activities and the young residents who are shifting toward hospitality and tourism. The study area, covering over 800 km2 in Sardinia, is half forested and has landscape interest. During the research, genotypes of Quercus ilex L. with sweet acorns and richer in polyphenols were discovered, the oldest ecosystem services in those rural communities. Collaborative monitoring focused attention on the benefits of plants in different habitats: 53% of these have been known to livestock farmers as food for local breeds of goats and pigs; 15% are officinal and 13% endemic. They have also been used in human nutrition within the Mediterranean diet, and they have landscape and garden interest. The study analyzes numerical data from a critical and educational perspective. These data serve as indicators of ecosystem health for the purpose of sustainable management policies, and attest to collaborative monitoring as a tool for analyzing human activities and the necessary balance between profit and biodiversity conservation, given the current challenging climate change conditions.

Historically the use of artificial intelligence (AI) in paediatrics dates back to 1984 with the intro-duction of a computer-assisted medical decision-making system called SHELP. Since then, research on the use of AI in pediatrics has become much more popular, and the number of reviewed publications largely increased. Consequently, a need for a holistic research landscape enabling researchers and other interested parties to gain insights into, deepen their knowledge about or inform them about the use of AI in pediatrics arised. To fill this gap a novel methodology named synthetic knowledge synthesis was applied. Using SKS we identified most prolific countries, in-stitutions, source titles, funding agencies, research themes, research gaps and hot spots.

The aim of this study was to explore the perceived impact of artificial intelligence (AI) on radiographers’ activities and profession in Switzerland. A survey conducted in the UK, translated into French and German, was disseminated through professional bodies and social media. The participants were Swiss radiographers (clinical/educators/ researchers/students) and physicians working within the medical imaging profession (radiology/nuclear medicine/radiation-oncology). The survey covered five sections: demographics, AI-knowledge, skills, confidence, perceptions about the AI impact. Descriptive, association statistics and qualitative thematic analysis were conducted. A total of 242 responses were collected (89% radiographers; 11% physicians). AI is being used by 43% of participants in clinical practice, but 63.8% of them did not feel confident with AI terminology. Participants viewed AI as an opportunity (57.2%), while 18.5% considered it as a threat. The opportunities were associated with streamlining repetitive tasks, minimizing errors, increasing time towards patient-centred care, research, and patient safety. The significant threats identified were reduction on work positions (22.6%), decrease of the radiographers’ expertise level due to automation bias (16.4%). Participants (68.3%) did not feel well trained/prepared to implement AI in their practice, highlighting the non-availability of specific training (87.6%). 93% of the participants mentioned that AI education should be included at undergraduate education program. Although most participants perceive AI as an opportunity, this study identified areas for improvement including lack of knowledge, educational supports/training, and confidence in radiographers. Customised training needs to be implemented to improve clinical practice and understanding of how AI can benefit radiographers.

The study explores the value of indigenous knowledge in mitigating and adapting to the effects of climate change in rural communities of Uganda. It provides an overview of how rural farmers use traditional and indigenous knowledge to predict weather changes to inform their adaptation and mitigation plans. The study intended to investigate how climate change effects were managed by the rural communities of Bakiga using indigenous knowledge and practices. Specifically, it sought to establish how local farmers’ innovations and practices were used to mitigate the effects of climate change and enhance food security. A case study research design with triangulation of data sources was employed. The study was conducted in Rukiga District, southwestern Uganda, where rural farmers were interviewed to explore their practices for reducing the effects of climate change to enhance food security. Thematic data analysis was used to get participants’ experiences and views regarding indigenous knowledge and effects of climate change mitigation. The findings indicate that rural farmers have indigenous practices of mitigating the effects climate change and contributing to the Sustainable Development Goals (SDGs).

There is a noticeable absence of health education among college students. This study aimed to evaluate the extent of general nutrition knowledge among Chinese university students and explore its association with eating attitudes. Data were collected from a group of 273 students in Spring of 2023, using a valid and reliable research instrument consisting of three sections: demographic variables, the General Nutrition Knowledge Questionnaire (GNKQ), and the Eating Attitudes Test (EAT-26). The results were analyzed using SPSS, with correlations and t-tests to examine the relationships between nutritional knowledge and dietary attitudes. Furthermore, the present study employed the Random Forest (RF) algorithm, a machine learning technique, utilizing the Mean Decrease Impurity (MDI) method to investigate the influence of various features on participants' eating attitudes. The findings revealed that Chinese university students had an average accuracy of over 60% in their nutritional knowledge, but their understanding of the relationship between diet and disease still needs improvement. Moreover, male students had significantly lower nutritional knowledge than female students, and there was a positive correlation between nutritional knowledge and parents’ income. The study also found a significant correlation between the level of nutritional knowledge and eating attitudes. RF results indicated that family income level exhibited the most substantial impact on the eating attitudes of the participants. The study highlights the need for nutrition education curriculum developers to focus more on improving students' nutritional knowledge, with particular attention given to male students, low-income individuals, and those with an abnormal BMI.

The rapid evolution of information and communication technologies, notably the Internet, has yielded substantial benefits while posing challenges to information system security. With an increasing frequency of cyber threats—from unauthorized access to data breaches—the digital landscape's vulnerability is evident. Addressing the financial impact of cybercrime, this study delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cybersecurity. Analyzing advancements and outcomes, the research explores practical techniques for anomaly detection, threat prediction, and automated response. By investigating prior research and real-world implementations, the study provides valuable insights into the potential of AI/ML, uncovering current trends, challenges, and prospects in enhancing cybersecurity tactics amid a dynamically changing threat landscape.

Seafloor mapping is crucial for sustainable Ocean management, but the distribution, drivers and origins of deep-sea biodiversity remain uncharted at global scales. The deep ocean is the largest and least-explored ecosystem on Earth, and the greatest source of unknown lifeforms. This con-tribution is firstly the story of a merge between exploration, science, education and technology for humankind cultural heritage advancement in one of the formally recognized protected areas of the Pacific Ocean. The data herein described are the outcome of a series of underwater surveys fi-nalized during the 2023 Exploration Campaign onboard E/V Nautilus, led by the Ocean Explo-ration Trust within the Papahānaumokuākea Marine National Monument, which is the largest conservation area established in the U.S. and one of the largest marine protected areas of the world. Our findings provide an advanced approach to scientific research combining the tradi-tional approach of marine sciences to the cultural and social dimension of local traditions, in or-der to empower the indigenous people in the underwater cultural heritage knowledge advance-ment process, aimed at deep-sea biodiversity detection and unexplored seafloor surveying. Be-side the high potential for this human dimension inclusion, the indigenous voices allowed to in-tegrate the mere scientific aspects commonly shared in every at-sea exploration, with the cultural values and practices respected from the local people who traditionally lived in sync with the Ocean for millennia. As a result, this approach allowed to enforce a trust relationship between the science world and some local representatives who joined the exploration onboard, providing an added value to the extant expertise.

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.

With global climate change and resource constraints looming large, sustainable design has become a focus of attention for all industries. The purpose of this study was to investigate the attitudes and behavioural intentions of undergraduate fashion design students towards sustainable fashion practices. Undergraduate fashion design students (n = 441) from three universities in Guangdong Province, China, participated in this survey. By constructing different scales of environmental knowledge, attitude towards sustainable fashion practice and behavioural intention towards sustainable fashion practice, and by using structural equation modelling to analyse the relationship between environmental knowledge and sustainable fashion practice, the following conclusions were drawn: (1) environmental knowledge has a positive effect on attitude towards sustainable fashion practice (p＜0.05); (2) environmental knowledge has a positive effect on behavioural intention towards sustainable fashion practice (p＜0.05); (3) Subjective norms has a positive effect on behavioural intention towards sustainable fashion practice (p＜0.05). (4) Attitudes towards sustainable fashion practice play a mediating role in the relationship between environmental knowledge and intentions towards sustainable fashion practice behaviour (p＜0.05). The findings of this study have implications for environmental education for undergraduate fashion design students and for sustainable production in companies.

The phenomenon of missing data can be seen everywhere in reality. Most typical attribute reduction models are only suitable for complete systems. But for incomplete systems, we can’t obtain the effective reduction rules. Even if there are a few reduction approaches, the classification accuracy of their reduction sets still needs to be improved. In order to overcome these shortcomings, this paper firstly defines the similarities of intra-cluster objects and inter-cluster objects based on the tolerance principle and the mechanism of knowledge granularity. Secondly, attributes are selected on the principle that the similarity of inter-cluster objects is small and the similarity of intra-cluster objects is large, and then the knowledge granularity attribute model is proposed under the background of clustering; Then, the IKAR algorithm program is designed. Finally, a series of comparative experiments about reduction size, running time and classification accuracy are conducted with twelve UCI data sets to evaluate the performance of IKAR algorithms, then the stability of Friedman test and Bonferroni-Dunn test are conducted. The experimental results indicate that the proposed algorithms are efficient and feasible.

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).

Stroke is a global leading cause of death and disability. Knowledge of related guidelines is crucial for Emergency Department (ED) staff, influencing early diagnosis and timely treatment. We investigated Greek-Cypriot ED healthcare professionals' (nurses and physicians) knowledge in recognizing and managing stroke. A descriptive cross-sectional study spanned November 2019 to April 2020, encompassing four private and seven public EDs in the Republic of Cyprus. The data were collected through a self-reported questionnaire developed by the research team, consisting of 37 questions. Eight questions focused on sociodemographic and employment characteristics, 28 assessed knowledge in stroke recognition and management (each item was equally weighted without deliberate prioritization), and one question addressed self-assessment of knowledge in stroke care. 255 nurses [Response Rate (RR): 74.1%] and 26 physicians (RR: 47.3%) completed the questionnaire. The average correct response rate was 12.9 out of 28 statements (SD: 4.2), with nurses and physicians scoring 12.6 (SD: 4.1) and 15.7 (SD: 4), respectively. Work experience significantly influenced stroke knowledge, with all groups demonstrating superiority over those with less than one year of experience. Participants with previous training scored an average of 1.45 additional correct answers while educational attainment did not significantly influence stroke knowledge. Investigating stroke knowledge among Emergency Department nurses and physicians in the Republic of Cyprus revealed significant deficits. The study stresses targeted interventions, including, education, yearly examinations, workshops with hands-on training, and repeated training, to address these gaps and enhance the overall stroke care capabilities of the healthcare professionals.

In the realm of artificial intelligence, knowledge graphs have become a fascinating area of research. Relationships between entities are depicted through a structural framework in knowledge graphs. In this paper, we propose to build a domain-specific medicine dictionary (DSMD) based on the principles of knowledge graphs. Our dictionary is composed of structured triples, where each entity is defined as a concept, and these concepts are interconnected through relationships. This comprehensive dictionary boasts more than 348,000 triples, encompassing over 20,000 medicine brands and 1,500 generic medicines. It offers a groundbreaking approach to storing and accessing medical data. Our dictionary facilitates various functionalities, including medicine brand information extraction, brand-specific queries, as well as queries involving two words or question answering. We anticipate that our dictionary will serve a broad spectrum of users, catering to both human users, such as diverse range of healthcare professionals as well as AI applications.

The study analyzed Ecuador's presence in the Web of Science (WoS) post-pandemic from a multivariate dynamic, highlighting a significant transformation in scientific communication towards open access (OA). The methodology integrated multivariate statistical techniques, using R and specialized tools, to process 9,085 relevant articles, 52% of the Ecuadorian publications in WoS between 2019 and 2021 were open access. This movement reflects a commitment to quality research and accessibility, showing an increase in publications and citations under OA. The research underscores the importance of establishing robust national OA policies in Ecuador to promote scientific collaboration and an equitable distribution of knowledge. Despite funding challenges and the lack of specific regulations, educational institutions in Ecuador have maintained a constant scientific output, adapting to the global environment and the demand for transparent information. The study concludes that there is an urgent need for a holistic approach to OA in the country, recognizing the progress made despite the difficulties.

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.

With the emergence of blockchain and smart contracts, traditional digital applications 1 such as identity management, supply chain management, banking and finance etc. are expected 2 to be transformed into Decentralized Applications (DApps). Blockchain interoperability is a new 3 and exciting aspect of blockchain technology that is quickly gaining popularity in many industries. 4 However, the widespread use of blockchains has not yet been achieved because DApps running on the 5 blockchain using smart contracts require access to authentic off-chain data. Users are more conscious 6 of their personal data privacy and consequently are more reluctant to share their data, posing a 7 challenge in the adoption of DApps. To solve this challenge, we present a novel quantum-resistant, 8 data authentication and zero-knowledge proof scheme named zk-DASTARK. Based on our proposed 9 scheme, we present a novel framework which can be used to feed authenticated off-chain data to 10 DApps without compromising user data privacy. Our proposed framework is quite efficient against 11 well-known off-chain data authentication frameworks. The proposed framework is implemented on 12 state-of-the-art quantum-resistant IOTA Blockchain and is highly efficient as it takes approximately 13 60 ms to generate a proof and approximately 10 ms to verify a proof.

Background:. Knowledge and positive perception of climate change are crucial for public participation in activities that contain climate change. Limited studies, particularly in South Africa have reported on the knowledge and perceptions of climate change by university students. Study Aim: This study aimed to assess the level of knowledge and perceptions of climate change by the science students at the University of South Africa (UNISA). Methods: This was a quantitative cross sectional survey, undertaken among science students using questionnaire emailed to 500 students in the School of Science through Unisa email system. Bivariate logistic regression was used to test the association between socio-demographics, level of knowledge and perceptions of climate change. Results: A total of 420 students participated in the study. They were predominantly male (70.3%), mostly younger than 35 years of age. Most of the respondents were in the 2nd (34.7%) and 3rd (33.8%) year of study. Majority of respondents (97.9%) knew and perceived (93.6%) that climate change was happening. Overall, 93.8% of respondents had an excellent knowledge and basic concepts of climate change. Conclusion: The majority of students were knowledgeable and had positive perceptions about the basic concepts of climate change. However, the year of study was not significantly associated with positive perceptions of climate change. There is need to include climate change modules in South African university curriculum.

Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a Semantic Framework for detecting problems in ADLs execution, monitored through Smart Home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed Framework combines multiple Semantic Web technologies (i.e. ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations forming a Knowledge Graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviours in ADLs execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people in the spectrum of dementia, by offering a comprehensive way for clinicians to describe problematic behaviors in the every-day life of an individual.

Background: Maternal mortality remains dire in Nigeria, claiming 40,000 lives yearly due to pregnancy complications. Factors like limited healthcare access, poverty, and early marriage worsen the crisis. Despite efforts to improve skilled birth attendance, Nigeria struggles to meet MDG targets, underscoring the urgent need for more effective interventions. Objectives: The objective of the study is to assess and understand the knowledge of community members regarding the risk factors influencing maternal mortality in the Ede South Local Government Area of Osun State, Nigeria. Methodology: A descriptive cross-sectional study was conducted. The study methodology involves employing the Leslie Kish formula to select 185 participants from the Ede South Local Government Area in Osun State, Nigeria. The selection process involved the use of a systematic sampling technique to ensure a representative sample based on population demographics and characteristics, ensuring a balanced and accurate selection process. Quantitative data was collected through a self-administered questionnaire and analysed using SPSS version 21. Results: The study encompassed 185 participants. 64.9% females with a mean age of 27.3 + 8.3 years. Among them, 60.5% were single, 46.6% being students, 55.7% held a BSc degree, 64.9% were Yoruba and 61.1% identified as Christian. 91.4% were aware about maternal mortality, 74.6% recognized maternal deaths in reproductive-aged women. Participants identified that maternal deaths mostly occur during labour (91.4%). Direct causes, such as bleeding (88.1%), unsafe abortion (88.6%), infection (49.2%), and hypertension (64.9%), were recognized. Results indicated substantial awareness concerning specific risk factors: lack of education (98.9%), poverty (91.6%), poor health care services in facilities (94.0%), alcohol consumption (87.6%) and poor transportation (78.4%). Conclusion: Findings revealed good awareness of maternal deaths and critical factors such as ANC impact and direct causes of maternal mortality like bleeding and unsafe abortion. However, gaps persisted in SDG awareness, suggesting the need for targeted interventions to enhance overall understanding and address identified knowledge gaps.

One of the current challenges in Higher Education is the development of students’ emotional competencies, and consequently, how these competencies can be trained. Moreover, this must be addressed within the UN framework of inclusive, equitable, quality education (United Nations Agenda 2030) (SDG 4). To address this challenge, the present study set the following objectives: (1) to analyze pre-service teachers’ level of interest in EI and their perceived level of general EI knowledge, (2) to examine their self-perception of certain variables of emotional knowledge, according to their degree program and year of studies, (3) to analyze the relationship between these emotional knowledge variables and perceived emotional intelligence (EI); and (4) to study the predictive capacity of the study variables on future teachers' emotional knowledge variables. The study sample contained a total of 434 university students from three degree programs at two universities in Spain, of which 82.70% were women, and the mean age was 20.91 years. Two instruments were used: an ad hoc questionnaire, which assessed perceived emotional knowledge according to three variables (General knowledge of EI, where level of training in EI is considered; Intrapersonal knowledge of EI; and Theoretical-practical knowledge related to teaching), and the Trait Meta-Mood Scale (TMMS-24), which assesses metaknowledge of emotional states across three variables (Attention, Clarity, and Repair). Results of the study suggest that there are differences in perceived EI knowledge according to students' year of studies and according to their degree, and indicate that students present high interest, while their perceived level of knowledge is low. In addition, emotional clarity is the EI variable that was identified as being important in the emotional knowledge of future teachers. This study shows evidence of the importance of social-emotional competencies in initial teacher training.

In the evolving landscape of Portfolio Management (PM), the fusion of advanced machine 1 learning techniques with traditional financial methodologies has opened new avenues for innovation. 2 Our study introduces a cutting-edge model combining Deep Reinforcement Learning (DRL) with 3 a Non-stationary Transformer architecture. This model is specifically designed to decode complex 4 patterns in financial time series data, enhancing portfolio management strategies with deeper insights 5 and robustness. It effectively tackles the challenges of data heterogeneity and market uncertainty, 6 key obstacles in PM. Our approach integrates key macroeconomic indicators and targeted news 7 sentiment analysis into its framework, capturing a comprehensive picture of market dynamics. This 8 amalgamation of varied data types addresses the multifaceted nature of financial markets, enhancing 9 the model’s ability to navigate through the complexities of asset management. Rigorous testing 10 demonstrates the model’s efficacy, highlighting the benefits of blending diverse data sources and 11 sophisticated algorithmic approaches in mastering the nuances of PM.12

Transitioning towards sustainable food systems and sustainable diets is critical in reducing environmental impact and achieving the Sustainable Development Goals established by the United Nations General Assembly. This transition requires collaborative efforts across multiple stakeholders, including the general public’s knowledge and attitudes. To assess the knowledge and attitudes towards food sustainability we carried out a cross-sectional study among Kuwait University students with a total of 278 participants aged ≥18 years (78% females and 22% males). A validated questionnaire was used for this study. Most respondents (&gt;70%) recognized the concept of “local food” and “environmental impact.” The concept “green water-blue water” was least recognized among respondents (75%). Participants perceived that a sustainable diet was rich in vegetables, included plenty of fresh products and was affordable. Participants (&gt;66%) perceived that a positive impact of meat and its derivatives towards sustainability, a misconception most common among males. Overall, participants with more knowledge about environmental concepts showed favorable attitudes towards a sustainable diet. Women showed more interest in changing their current dietary habits towards food sustainability. In conclusion, despite participants’ positive attitude towards food sustainability, lack of understanding persists, necessitating public health interventions aimed at educating consumers about sustainable diets and its impact on the environment.

The transition to sustainability requires major behavioural changes. This requires a complete rethinking of ecosystems and their interconnections, reassessing environmental issues, and a behavior change manifesting. Thus, understanding how sustainability Knowledge, Attitudes, and Practices (KAP) change is essential to sustainability as they shape human behaviour. The main aim of this study was to investigate the constructs of sustainability KAP among university and non-university populations. Data were collected using a structured questionnaire of items measured on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The questionnaire was distributed to diverse participants, ensuring a broad representation of views on sustainability. There are ten sociodemographic variables, 344 respondents in UAE where the female (28.5%) and male (13.7%), (58.1%) participants did not belong to the UAE university and their age fall into the 18-24 years old. The mean scores indicate a positive orientation towards sustainability, with Knowledge (M = 4.2272), Attitudes (M = 4.2650), and Practices (M = 3.8198) all above the midpoint and the UAE university students showed more knowledge, attitude and practices scores ‎than others population. Associations between KAP were significant at a significant level of p &lt; 0.05. This indicates that the next generation will have more environmentally KAP.

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.

It is essential to deal with data scattered among heterogeneous information sources, which can be structured, semi-structured, or unstructured, to give consumers a cohesive perspective of the data. Information gathering is challenging as a result, and one of the major causes of this is that data sources are developed to support specific applications. A method to simplify this procedure is to use ontology representation as an intermediate step. An ontology represents a knowledge structure that reasonably reflects the real world's complexity and is ideally used in many industries today. This research aims to enhance the functionality of a global ontology system by implementing and integrating a natural language query system. Leveraging NLP-based approaches, we constructed SPARQL queries for our ontologies to facilitate natural language translation into a well-structured query format. A dedicated query component was developed to transform natural language questions into SPARQL queries, with the selection of the optimal query graph to generate the final queries. In the subsequent phase, we explored the integration of distinct sentence encoders to improve latent sentence representations during query construction. The generated SPARQL queries were executed on the global ontology output and then translated into source-specific questions. This innovative approach enables unified access to heterogeneous data sources through a user-friendly natural language querying interface.

Aging is a major risk factor for the higher incidence and prevalence of chronic conditions, such as cancer, cardiovascular and metabolic diseases, and neurodegenerative diseases. Closely linked with the onset of all these age-related diseases is immunosenescence, a condition defined as progressive reconfiguration of the immune functions leading to an increased susceptibility to infectious diseases, poorer response to vaccination, increased prevalence of cancer, rheumatoid arthritis, and other chronic diseases characterized by a pro-inflammatory state named inflammaging. Given that immunosenescence frames age-related diseases, it is natural to ask how these illnesses are related under the context of innate and adaptative components of the immune system. Knowledge processing by Formal Concept Analysis (FCA) of age-related diseases framed in an immunosenescence multivalued context shows that a hierarchy among these diseases emerges with a subconcept-superconcept nested structure, with two clusters, first made of metabolic diseases, cancer, and cardiovascular diseases. Second metabolic and neurodegenerative diseases and rheumatoid arthritis. Metabolic diseases seem to be a pivot illness and they must be a primary target given their global incidence and prevalence and their cost-effective treatments like regular physical activity, weight loss, healthy diet, stopping smoking, and reducing or managing stress. Another implication of the ontological analysis of age-related diseases is about the nature of immunosenescence, suggesting that initial symptoms of age-related diseases are mainly related to innate immune mechanisms, and developing of severe symptoms of age-related diseases like cancer, cardiovascular or neurodegenerative diseases depends on the activation of acquired immunity processes.

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.

Background: Injury is an important cause of mortality and morbidity.(1). FA is the immediate assistance provided to a suddenly injured or sick person until professional help arrives or that person has recovered.(2), could be a potential factor differentiating between life and death in RTA . Many lives would be saved if proper first aid is provided early. (3) In the middle east, there are limited studies that explore knowledge and practices towards first aid needed during RTA. Objective: The main aim of this study is to investigate the knowledge and attitude towards first aid among drivers in Sharjah. Methodology: A cross-sectional study was conducted through a self-administered questionnaire written in Arabic and English languages. Participants were 18 years or older holding a driving license in the city of Sharjah. Paramedics and certified participants with a certification from authorized organizations were excluded . A non-proportional convenient sampling method was used. A total of 343 participants have completed the questionnaires. Data was entered and analysed using SPSS. Results: Majority of the study participants were less than 40 years old and (56.76%) were males. Most participants(94.2%) indicated that they know the term ‘First Aid’. However, (49.73%) of the participants knew the ambulance number and (52.43%) of the participants indicated that the victim should be moved to the hospital without the ambulance. With regards to attitude, 34.86% were not willing to provide first aid in the case of RTA. The main reason was lack of knowledge followed by fear of legal consequences. Most participants (83,78%) were willing to participate in first aid classes to improve their knowledge and attitude. Conclusion: Most participants are aware of the basic term of first aid; however, they are not willing to provide first aid in cases of RTA due to lack of knowledge and fear of legal consequences. Based on these findings we recommend providing first aid classes for new drivers and to obtain certification. References: 1 and 3. Midani, O., Tillawi, T., Saqer, A., Hammami, M., Taifour, H. and Mohammad, H., 2020. Knowledge And Attitude Toward First Aid: A Cross-Sectional Study In The United Arab Emirates. 2.Van de Velde S, Heselmans A, Roex A, Vandekerckhove P, Ramaekers D, Aertgeerts B. Effectiveness of nonresuscitative first aid training in laypersons:A systematic review. Ann Emerg Med 2009;54:447-57, 457e1-5.

Traffic congestion and frequent traffic accidents have become main problems affecting urban traffic. Effective location prediction of vehicle trajectory can help alleviate traffic congestion, reduce the occurrence of traffic accidents, and optimize the urban traffic system. Vehicle trajectory is closely related to the surrounding Points of Interest (POI). POI can be considered as the spatial feature and can be fused with trajectory points to improve prediction accuracy. A Local Dynamic Graph Spatiotemporal- Long Short-Term Memory (LDGST-LSTM) is proposed in this paper to extract and fuse the POI knowledge and realize next location prediction. POI semantic information is learned by constructing the traffic knowledge graph, and spatial and temporal features are extracted by combining Graph Attention Network (GAT) and temporal attention mechanism. Moreover, the weights of POI that influence location prediction are visualized to improve the interpretability of the proposed model. The effectiveness of LDGST-LSTM is verified on two datasets, including Chengdu taxi trajectory data in October 2018 and August 2014. The accuracy and robustness of the proposed model are significantly improved compared with the benchmark models.

The new Common Agricultural Policy of the European Union (EU) (2023-2027) envisages a stronger Agricultural Knowledge and Innovation System (AKIS) that will unite all relevant ac-tors in a co-creation process to ensure knowledge flows within and across Member States. A sig-nificant and increasing body of literature focuses on the strategy and processes of strengthening AKIS. However, there is a relative gap in research related to the role of AKIS in achieving sus-tainability in the farming sector. The purpose of this paper is to provide an overview of the advancements so far of AKIS across the EU when it comes to facilitating the sustainable management of natural resources (SMNR). The paper builds upon a comprehensive review of rel-evant literature during the last 15 years covering all 28 EU countries (including the United King-dom until 2019). We conclude that AKIS architecture in most EU countries lacks a comprehensive structure promoting SMNR. Based on the results we identify countries with relatively high inten-sity of research and development projects linking AKIS to SMNR and where SMNR is better in-tegrated to AKIS. Our results can be of interest to the design of stronger AKIS during the new programming period.

IIt is important that Master’s Degree students in psychology, regardless of their future specialisa-tion, know about Attention Deficit Hyperactivity Disorder (ADHD). The aim is evaluate and an-alyse the ADHD-related knowledge held by students of Early Childhood Education and Primary Education. The findings that were obtained in this study (on the chronicity of the disorder, changes in manifestations throughout the life cycle, and the importance of early and multidisci-plinary intervention) have clear implications in relation to the specific training in ADHD for fu-ture teachers. It is essential to broaden the specific training in relation to ADHD in studies for the Master’s Degrees in Early Childhood Education and Primary Education and through contin-uous training.

Aiming to investigate the impact of strategic knowledge disclosure on the continuous realization of digital innovation in enterprises, this study examines data from 168 listed companies across three high-tech industries in China from 2012 to 2021. By examining investor trust, consumer trust, worker trust, and rival trust as mediating variables within the market trust dimension, this research explores the impact mechanism of strategic knowledge disclosure on the sustainability of input and output in digital innovation. The findings reveal that strategic knowledge disclosure significantly promotes both input and output sustainability in digital innovation. Specifically, investor trust positively mediates the relationship between strategic knowledge disclosure and the sustainability of digital innovation input. Similarly, consumer trust and worker trust positively mediate the association between strategic knowledge disclosure and both input and output sustainability in digital innovation. However, it is important to note that rival trust negatively mediates the link between strategic knowledge disclosure and both input and output sustainability in digital innovation. The research findings possess significant theoretical value and provide valuable guidance for promoting the sustainable development of digital innovation in enterprises.

With 500+ papers and 20+ books spanning many scientific disciplines, Mark Burgin has left an indelible mark and legacy for future explorers of human thought and information technology professionals. In this paper, I discuss his contribution to the evolution of machine intelligence using his general theory of information (GTI) based on my discussions with him and various papers I co-authored during the past eight years. His construction of a new class of digital automata to overcome the barrier posed by the Church-Turing Thesis, and his contribution to Super-Symbolic Computing with Knowledge Structures, Cognizing Oracles, and Structural Machines are leading to practical applications changing the future landscape of information systems. GTI provides a model for the operational knowledge of biological systems to build, operate, and manage life processes using 30+ trillion cells capable of replication and metabolism. The schema, and associated operations derived from GTI are also used to model a digital genome specifying the operational knowledge of algorithms executing the software life processes with specific purposes using replication and metabolism. The result is a digital software system with a super-symbolic computing structure exhibiting autopoietic and cognitive behaviors that biological systems also exhibit. We discuss here one of these applications.

A teoria diz que o número interage com os espaços com base no significado da matemática, desta forma é possível obter conhecimento numérico. A teoria mostra a possibilidade de usar espaços relacionados entre si para explicar as razões pelas quais dois números pares formam um par, dois números ímpares formam um par, um número par e um ímpar formam um número ímpar, três números ímpares formam um número ímpar, três números pares formam um número ímpar. um par, um ímpar e dois pares formam um ímpar, dois ímpares e um par formam um par.

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.

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.

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.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence and structural knowledge of drugs, targets, and pockets using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model’s performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, the supremacy of S2DTA became strikingly apparent. It showcased a noteworthy 25.2% reduction in Mean Absolute Error (MAE) and an impressive 20.1% decrease in Root Mean Square Error (RMSE). Furthermore, S2DTA exhibited substantial advancements in other pivotal metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2. These metrics experienced remarkable increments of at least 19.6%, 17.5%, 8.1%, and a remarkable 49.4%, respectively. Finally, we conducted interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism, fully proving that S2DTA is a valuable and accurate tool for predicting DTA. For further exploration, the source data and code repository can be accessed at https://github.com/dldxzx/S2DTA.

BACKGROUND: Cervical cancer is the second most frequent cancer among women worldwide between 14 and 49 years of age including Nigeria. Figures have greatly reduced in developed countries after the introduction and implementation of effective screening and vaccination programs which is greatly undeveloped and inefficient in Nigeria and other developing countries at large. OBJECTIVES: The study assessed the knowledge and attitude of reproductive age women towards cervical cancer prevention in selected tertiary institutions in Osun State. METHODOLOGY: The study was a descriptive cross-sectional study carried out among reproductive age women in selected tertiary institutions in Osun State, Nigeria. A probability based multistage sampling technique was adopted as the sampling technique for the study. Data was collected using a semi-structured, self-administered and interviewer guided questionnaire. RESULTS: Age of respondents was 25.305±8.195. 313(79.0%) of the total respondents were Christians, and 83(21.0%) of the respondents were Muslims. For the overall knowledge score, only 52.0% of the respondents had good knowledge while 48.0% of the respondents had poor knowledge. 52.0% exhibited negative attitude towards cervical cancer prevention while 48% exhibited positive attitude towards cervical cancer prevention. Only 23% of the respondents had taken part in screening and vaccination towards cervical cancer prevention while 77% of the respondents had not. CONCLUSION: The knowledge of reproductive age women towards cervical cancer prevention was above average while their attitude towards cervical cancer prevention was low. This issue could be addressed by increasing the awareness of the effects of cervical cancer among reproductive age women in the country.

Measles remains a highly contagious and potentially severe infectious disease, necessitating high vaccine coverage. However, misinformation and measles vaccine hesitancy/resistance posed significant challenges to achieving this goal. The COVID-19 pandemic further exacerbated these challenges, leading to a measles outbreak in Jordan in 2023. This study aimed to investigate the acceptance of the Measles-Rubella (MR) vaccine among parents in Jordan and identify its associated determinants. This cross-sectional questionnaire-based study was conducted using a previously Arabic-validated version of the Parental Attitudes towards Childhood Vaccines (PACV) survey instrument. Data collection took place in October 2023, and the final study sample comprised a total of 391 parents, with mothers representing 69.8% of the participants (n = 273). The majority of participating parents expressed either resistance (n = 169, 43.2%) or hesitancy (n = 168, 43.0%) towards MR vaccination, while only 54 participants (13.8%) expressed MR vaccine acceptance. Multivariate analysis revealed that trust in vaccine safety/efficacy, behavior, and having fewer offspring were significantly associated with MR vaccine acceptance. The current study revealed a concerning level of MR vaccine hesitancy/resistance among parents in Jordan. Urgent and targeted interventions are strongly recommended to address this issue, including mass campaigns aimed at building trust in the MR vaccine safety/efficacy. Additionally, there is an urgent need for effective public health initiatives to ensure sufficient measles vaccine coverage to prevent future outbreaks of this serious disease.

Background: Exclusive breastfeeding (EBF) for the first 6 months of life improves the growth, health and survival status of newborns. Breastfeeding has been accepted as the most vital intervention for reducing infant mortality and ensuring optimal growth and development of children. Objectives: The general objective was to identify the factors affecting exclusive breastfeeding among working class women in selected wards in Egbedore Local Government in Osun state, Nigeria. Methodology: This was descriptive cross-sectional study. Leslie Kish formula was used for sample size calculation and multistage sampling was used in selecting 316 working class women for the study. A structured, self-administered questionnaire was used for data collection. Data was analyzed using SPSS version 21.0 and level of significance was set at 5%. Results: 58% of the respondents were between the ages of 25-30. 72% demonstrated good understanding of exclusive breastfeeding based on the 6 months WHO recommendation for exclusive breast feeding, 25.9% had a fair knowledge and awareness of exclusive breastfeeding. 63.3% of the working class mother has a good practice of exclusive breast feeding, 19.6% of the women moderately practices exclusive breastfeeding while 17.1% poorly practices exclusive breastfeeding. Less than 3-months maternity leave for mothers, lack of crèche closer to place of work and lack of breastfeeding break were identified as barriers to exclusive breastfeeding practices among the working class women. The findings demonstrated a significant relationship between knowledge and practices of exclusively breastfeeding among working class women (P-value .009). Conclusion: This study demonstrated that working class women had good knowledge of exclusive breastfeeding and this influenced their breastfeeding practices. A good knowledge was associated with favorable attitudes to the practice of exclusive breast feeding. To derive long-term benefits from early childhood nutrition, interventions should focus on health education and community-based sensitization programs on the needs for mothers to engage in exclusive breast needing for global development.

Providing safely-managed sanitation/hygiene requires key competencies for education, training, service delivery, enterprise development and management, product and infrastructure design and development, construction and installation, governance, financing, research, etc. These strategic sanitation capacities will have to be built from higher education’s academic and professional programmes structured and designed to produce skilled and knowledgeable professionals and practitioners. This study aims to investigate the quality of the sanitation/hygiene management content of Environmental Health programmes; the adequacy of the existing central curricula; and the perception of environmental health sanitation/hygiene-trained professionals to determine the next phase for building knowledge and capacity of sanitation professionals in Nigeria through higher education institutions. A multilevel mixed method concurrent study was used for sampling and data collection with a multilevel perception analysis to examine the perceptions of students, lecturers and graduate alumni as well as employers/supervisors and clients/service-users of sanitation/hygiene related graduates. The main findings of this paper show limited understanding of the concepts of contemporary issues of sanitation/hygiene management; the central/national teaching and curricula were found to be seriously out of date; and clients/service-users were dissatisfied with the skills and knowledge levels of the graduates. The study concludes by recommending a national sanitation management higher education pathway to guide the provision of integrated sanitation/hygiene management education at higher education institutions (HEIs), to build effective, efficient, competent, and sufficient workforce for the country.

Plant biotechnology is dependent today on accessing broader plant genetic resources for food and agriculture (PGRFA) diversity for ensuring food security for our future. Heterogenous agricultural lands are considered today as hotspot for biodiversity, and they also exist in the hilly mountain areas of Romanian’s Carpathians. The scope of this review is to describe traditional home-hardens related to the land use of households and cultivated species from Moșna commune, Sibiu County, Romania. The survey was realized with the support of authorities and local stakeholders from 2010 up to 2019. The results of this survey revealed that maintenance of crop species structure and land use of traditional households is longer than 100 years. Consequently at least 15 traditional Saxon home-gardens of 120 taken into the study may be defined. Today these home-garden covers a media of 26% of the total area of a household and should be part of a surface ranging between 5,000 and 1,500 m2. The first Local Council Decision in Romania in 2019 supporting the recognition of landraces and home-gardens as heritage values was officially adopted supporting the need to more access bottom-up approaches for supporting biodiversity conservation.

The world is facing an energy crisis. Governments are seeking to provide universal energy access and guarantee energy security while trying to mitigate climate change. One possible solution is energy transitions towards low carbon energy systems. Among other things (physical infrastructure, public policy and regulatory enablers and knowledge and capacities) changes in the energy systems require a well informed and participative citizenship. Within this context the concept of energy literacy appears. Energy literacy is the understanding of how energy is generated, transported, stored, distributed and used, awareness about its environmental and social impacts and the knowledge to use it efficiently in the different sectors of the economy. This paper provides a systematic literature review in the Web of Science’s Core Collection. Most of the work done around energy literacy addresses its evaluation among different groups, particularly students at different levels, and the construction, application and evaluation of tools for improving energy literacy. Other frequently studied issues are the influence of energy literacy in decision making, its drivers and conceptual research about the topic. Energy enables citizens to effectively contribute to energy efficiency and sustainable development, nevertheless energy literacy is not strongly correlated to energy consumption habits.

Understanding the genetic architecture of a disease is crucial for development of valid diagnostic and therapeutic interventions. The analysis of genomic variations associated with pathological conditions is the starting point for uncovering disease-causing pathways (candidate processes). However, the complexity of intergenic and genetic-environmental interactions hinders the identification of pathogenic values of genomic changes. Furthermore, heredity, epigenetics and somatic mosaicism make the interpretation of genomic data even more sophisticated. To succeed, a variety of bioinformatic techniques are applied. Here, reviewing own and literature data, knowledge-based prioritization of genomic variations is described. Theoretical basis of the knowledge-based prioritization is given with a special regard to gene ontology, heuristics, hermeneutics (genomic hermeneutics) and analytics. Practical and methodological issues of prioritization using ontology- or pathway-based systems analysis are considered in the light of optimistic and realistic scenarios of cumulative phenotypic effects of the variome (the whole set of genomic variations in an individual or specific set of genomic variations for a phenotypic outcome). In the present communication, copy number variants (CNVs) in children with neurodevelopmental diseases are used as a practical foundation for the prioritization, inasmuch as these genome variations are systematically overlooked in the so-called NGS era. Nonetheless, it is highly likely that the prioritization is applicable to almost all types of genomic variations (e.g. chromosome abnormalities, gene mutations, functional synonymous variants etc.). The present methodology seems to be a valuable addition to current biomedical science widening the opportunities for medical genomics and genetics.

Keywords: Knowledge; Attitudes; Practices; Salt intake.

Despite the impressive performance of deep neural networks on many different vision tasks, they have been known to be vulnerable to intentionally added noise to input images. To combat these adversarial examples (AEs), improving the adversarial robustness of models has emerged as an important research topic, and research has been conducted in various directions including adversarial training, image denoising, and adversarial purification. Among them, this paper focuses on adversarial purification, which is a kind of pre-processing that removes noise before AEs enter a classification model. The advantage of adversarial purification is that it can improve robustness without affecting the model’s nature, while another defense techniques like adversarial training suffer from a decrease in model accuracy. Our proposed purification framework utilizes a Convolutional Autoencoder as a base model to capture the features of images and their spatial structure. We further aim to improve the adversarial robustness of our purification model by distilling the knowledge from teacher models. To this end, we train two Convolutional Autoencoders (teachers), one with adversarial training and the other with normal training. Then, through ensemble knowledge distillation, we transfer the ability of denoising and restoring of original images to the student model (purification model). Our extensive experiments confirm that our student model achieves high purification performance(i.e., how accurately a pre-trained classification model classifies purified images). The ablation study confirms the positive effect of our idea of ensemble knowledge distillation from two teachers on performance.

Objectives This article focuses on exploring the evolution of Palliative Care in Portugal. Ten years after the approval of its Basic Law, the aim was to investigate the quality of the path followed and the guidelines that could promote its development. Thus, this study sought to identify a) the goals of the current members of parliament concerning Palliative Care, b) the major priorities that should guide the development of Palliative Care in the coming decade, c) the facilitating or hindering factors to accelerating the process, and d) to propose consensually agreed measures for an integral development of Palliative Care within the health system. Methods Qualitative data analysis was performed from the reading of literature and interviews conducted via Zoom with several intentionally chosen participants, leading to the creation of a Focus Group. The NVivo® 10 was used for data processing and categorisation. Results Three key themes emerged concerning the current status of palliative care: the policymakers, the technicians, and the society. This first line of structuring is explained by a second set of categories, namely, a) knowledge about palliative care; b) Palliative care organisation from the policymakers’ perspective. In the technicians' domain: a) knowledge about palliative care, b) clinical training, and c) medical specialty. Finally, in society: a) knowledge about palliative care. Significance of the Results Advancing general education, increasing qualitative training for the different health professionals, reformulating laws supporting them, promoting flexibility of the implementation methodologies, and establishing medical specialty, are crucial to achieving the proposed goal.

Generative artificial intelligence (AI), in particular large language models such as ChatGPT have reached public consciousness with a wide-ranging discussion of their capabilities and suitability for various professions. Following the printing press and internet, generative AI language models are the third transformative technological invention with truly cross-sectoral impact on knowledge transmission. While the printing press allowed for the transmission of knowledge that is independent of the physical presence of the knowledge holder with publishers acting as gatekeepers, the internet added levels of democratization allowing anyone to publish, along with global immediacy. The development of social media resulted in an increased fragmentation and tribalization of on-line communities on their ways of knowing, resulting in alternative truths propagated in echo chambers. It is against this background that generative AI language models have entered public consciousness. Using strategic foresight methodology, this paper will examine the polemic proposition that the age of generative AI will emerge as an age of public ignorance.

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.

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.

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.

The overexploitation of forest resources in the charcoal production basin of the city of Lubum-bashi (DR Congo) is reducing the resilience of miombo woodlands and threatening the survival of the riparian as well as urban human populations that depend on it. We assessed the socio-economic value and availability of plant-based non-timber forest products NTFPs in the rural area of Lubumbashi through ethnobotanical (100 respondents) and socio-economic (90 respondents) interviews, supplemented with floristic inventories, in two village areas selected on the basis of the level of forest degradation. The results show that 60 woody species, including 46 in the degraded forest (Maksem) and 53 in the intact forest (Mwawa), belonging to 22 families are used as sources of NTFPs in both villages. Among these species, 25 are considered priority species. NTFPs are collected for various purposes, including handcrafting, hut building, and traditional medicine. Moreover, the ethnobotanical lists reveal a similarity of almost 75%, indicating that both local communities surveyed use the same species for collecting plant-based NTFPs, despite differences in the level of degradation of the miombo woodlands in the two corresponding study areas. However, the plant-based NTFPs that are collected from miombo woodlands and traded in the urban markets have significant economic value, which ranges from 0.5 to 14.58 USD per kg depending on the species and uses. NTFPs used for handicraft purposes have a higher economic value than those used for other purposes. However, the sustainability of this activity is threatened due to unsustainable harvesting practices that include stem slashing, root digging, and bark peeling of woody species. Consequently, there is a low availability of plant-based NTFPs, particularly in the village area where forest degradation is more advanced. It is imperative that policies for monitoring and regulation of harvesting, and promoting sustainable management of communities’ plant-based NTFPs priority, be undertaken to maintain their resilience.

Due to the inclusion of both the principles and divination methods, the study of Yijing (Yijing) becomes more complex. The complex data types, diverse semantic relationships, and unclear influencing mechanisms in Yijing pose challenges to its scientific research. Knowledge graph, as an advanced way of organizing knowledge, can provide technical support in exploring the hidden connections and knowledge structure within text resources. This paper proposes a method of constructing a Yijing knowledge graph based on Neo4j. By analyzing the concepts, entities, and various related relationships in Yijing, the method discusses the basic architecture, data model, and implementation steps of constructing the Yijing knowledge graph. It also extracts, integrates, and structures the Yijing knowledge, allowing for inquiries, associations, and reasoning about the knowledge and its various elements. By adopting a top-down approach, the paper reconstructs the fundamental knowledge system of the Yijing, including yin-yang, the five elements, generation and restriction, 64 hexagrams, and six lines(yao). It establishes a knowledge graph pattern layer and a data layer that encompass concepts, attributes, and relationships. With the technical support of the Neo4j platform, the visualization and retrieval of Yijing knowledge are realized, providing new ideas and methods for studying the mechanisms of the Yijing. The proposed method of constructing a Yijing knowledge graph can be expanded to other types of ancient scripture knowledge graph research and can be applied to the field of digital humanities, promoting in-depth cross-disciplinary research and integration.

The unsupervised domain-adaptive vehicle reidentification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain, however there are knowledge differences between the target domain and the source domain. To reduce domain differences, existing unsupervised domain-adaptive rerecognition methods generally require access to the source domain data to assist in re-training the target domain model. But for security reasons, in many cases, data between different domains cannot communicate. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a passive domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the "source-like samples". Then, it can effectively reduce the model knowledge difference and improve the model generalization performance. According to the experiment and testing of VeRi776 and VehicleID, two mainstream public data sets in this field, the rank-k and mAP indexes obtained are both improved, and are suitable for object re-recognition tasks without source data.

Abstract: Educating healthcare workers (HCWs) to take action against monkeypox (mpox) is an important part of public health prevention efforts. The study aimed to assess the knowledge, attitudes, and willingness to vaccinate against mpox among HCWs in the Kurdistan Region of Iraq. This study utilized an online cross-sectional survey that was disseminated via Google Forms between November, 1, 2022 and January, 15, 2023, employing a convenience sampling method. The researchers utilized logistic regression to ascertain the factors associated with knowledge, attitude and willingness to vaccinate. A total of 637 HCWs were included in the analysis (ages ranged between 21 and 51 years old). The mean overall score and standard deviation of the knowledge, attitude, and willingness assessment on mpox were 8.18±3.37 out of (0–16), 3.40±1.37 out of (0–5), 2.41±1.25 out of (0–5), respectively. The multivariate logistic regression analysis showed that HCWs who heard about mpox before 2022 had a higher level of knowledge (AOR: 4.85; 95% CI: 2.81-8.36; p < 0.001). In addition, those who had less than 1 year of practice had a positive attitude about mpox (AOR: 0.35; 95% CI: 0.20-0.59; p < 0.01). Finally, none of the variable groups had the capacity to predict willingness to be vaccinated against mpox. The research revealed that HCWs exhibit a relatively low level of knowledge and attitude towards mpox, as well as a low level of willingness to receive mpox vaccinations. Further, there is an urgent need to increase their knowledge and attitude, as the success of efforts to control the global epidemic depends on them.

Keywords: iron deficiency anemia; IDA; anemia; knowledge; awareness.

Despite air pollution being a leading cause of health issues in developing nations, public awareness and understanding of local air quality remains notably low. The present study assesses the perception, attitude, and environmental knowledge of local air pollution among adult urban residents (n=870) in a city with leading air pollution rates among cities of emerging economies: Astana, Kazakhstan. Structural equation modeling (SEM) was employed to investigate the causal relationship between perceived air quality, environmental literacy, and willingness to pay (WTP) for environmental protection. Findings indicate over 40% of residents neither consider the city being highly polluted nor recognize the association between air quality and adverse health outcomes, correlated with a generally low level of environmental literacy. The age, education, and health status of the participants significantly affected (p&lt;.001) their level of environmental knowledge and awareness. The SEM analysis indicates that knowledge is the major determinant in improving public awareness and perception of local air pollution (path value=0.626). Moreover, a close association between WTP and environmental attitude was also evident (path value=0.533). The findings of the present study may provide valuable insights for healthcare professionals, environmental researchers, and governmental institutions for implementing more effective public interventions to protect local air quality.

The concept of collecting data of people using a domestic space is not novel. However, the methods and processes used to decipher this raw data and transform it into useful and appropriate information (i.e., sequence, duration, and timing derived from domestic activities) have been challenging and the focus of many research groups. But how are the results of the decoded transposition received, interpreted and used by the various professionals (e.g., occupational therapists and architects) who consume the information? This paper describes the inclusive evaluation process undertaken, which involved architects, engineers and end-users (not the occupant, but the care team managing the occupant). Finally, our study evidence the importance of making accessible to a multidisciplinary audience spatial, temporal and sequential aspects derived from people's domestic routines. Shedding light on how a systematic approach to collecting, processing and mapping low-level sensor data into higher forms and representations can be a valuable source of knowledge for improving the domestic living experience.

The objectives of this study are threefold. First, we evaluate the applicability of the information–motivation–behavioral skills (IMB) model for predicting organic food purchase intention (OFPI) in adults with chronic conditions. Second, we examine the indirect effects of organic food knowledge, attitudes toward organic food purchase, and subjective norms on OFPI through self-efficacy. Third, we examine whether these indirect effects are moderated by gender and educational attainment. Data were collected from Indian adults with chronic conditions using a self-administered questionnaire. The results show significant indirect effects of organic food knowledge, attitude toward organic food purchase, and subjective norm on OFPI through self-efficacy. Moreover, the mediating effect of knowledge was moderated by gender and education attainment, with the effect being stronger for females and among individuals with a lower level of education. Organic food marketers, social marketers, and public health agencies promoting organic food consumption to people with chronic conditions should aim to increase their confidence in comprehending organic food. This study contributes to the literature by assessing the applicability of Fisher and Fisher’s (1992) IMB model in understanding behavioral intention toward organic food.

To learn what mothers know on newborn bloodspot screening (NBS), the procedure, and sources used, a pilot study was performed. An online questionnaire was developed, with a first part fo-cused on characteristics and the NBS procedure, a second on knowledge, information sources, and health care providers (HCP). This questionnaire was accessible until 200 answers were received. Characteristics of respondents were representative for the population. Mothers recalled verbal consent in 69.5%, 12.5% did not, 18% stated that no consent was requested. The ‘knowledge’ part contained 12 closed questions, 5 multiple-choice questions on sources, and assessment (5-point Likert) of the information transfer. The mean knowledge level was 7.2/12. Screening concepts (consequences, likelihood, sensitivity, carrier) and absence of notification of normal findings were well known. The fact that NBS is not compulsory and post-analysis sample handling were poorly known. Key HCPs were midwifes (80.5%) and nurses (38.5%). When the leaflet (44%) was pro-vided, the majority read it. Mean Likert scores were 3.36, 3.38, 3.11 and 3.35 (clarity, timing ap-propriateness, sufficiency, usefulness). The knowledge level and consent practices were reasonable well. Key HCP were midwives and nurses, leaflets were supporting. This should enable a quality improvement program to a sustainable NBS program in Flanders.

Zoonotic abortive diseases represent a significant health and economic risk for national public health. This cross-sectional survey was conducted from April to October 2021 among 200 selected small ruminant farmers in the three northern regions of Cameroon. Data collection was done through questionnaires administered by exchange with the herder and responses were coded and recorded on an Excel spreadsheet. The data were then analyzed with R software, version 2.13.0. An ANOVA test was used to assess significant differences in mean of Knowledge, Attitudes, Practices and Perception of zoonotic risks (KAPP) scores between regions. Pearson correlations were used to calculate the association between KAPP scores within regions. Small ruminant farmers surveyed had low mean scores for knowledge of abortive Toxoplasmosis and Chla-mydophilosis (0.1 ± 0.2), desirable attitude (0.32 ± 0.07), appropriate practice (0.36 ± 0.13) in managing abortion, and positive perception of zoonotic risks of Toxoplasmosis and Chlamydo-philosis in small ruminants (0.12 ± 0.33), respectively. KAPP was significantly (p<0.01) and posi-tively associated with knowledge (r=0.98) and risk perception (r=0.99). However, attitudes (r=0.06), practices (r=0.05) and risk perception of toxoplasmosis (r=0.07) and Chlamydophilosis (r=0.08) were not associated with farmers' knowledge. This study revealed significant knowledge gaps, low levels of desired attitudes, and high-risk behavioral practices. These results therefore call for capacity building of health professionals and farmers to better integrate the One Health concept in the management of neglected zoonotic diseases.

This study was aimed to identify perception, knowledge, attitude and nursing practice toward use of physical restraints among clinical nurses, and further examined the factors associated with nursing practice toward use of physical restraints. The research participants were 180 nurses from a urban general hospital located in Korea. Data were collected using self-report questionnaires and analyzed using t-test, ANOVA, Pearson correlation coefficients, and multiple regression. There were significant negative relationships attitudes towards the use of physical restraints with knowledge (r = -.32, p &lt; .001) and nursing practice (r = -.12, p = .107). Knowledge showed a positive correlation with nursing practice (r = .28, p &lt; .001). Factors affecting nursing practice of clinical nurses were identified as knowledge (β= .23), education experiences (Yes) (β= .18), and work unit (ICU) (β= .43). The explanation power of this regression model was 22% and it was statistically significant (F=7.45, p&lt;.001). The findings from this study suggest that knowledge, education experiences and work unit were the strongest predictor on nursing practice toward use of physical restraints. Thus, we propose that developing and applying evidence-based educational intervention programs by work unit to reduce the inappropriate use of physical restraints in hospitals are required.

Technology Readiness Levels (TRLs) is a well-established and widely used approach for defining the readiness of new technology. To this extent, it assesses technology maturity against specific benchmarks, ranging from 1 (concept) to 9 (market solution). Although this is a useful classification service, allowing us to establish a common language, there are cases where we find that this conceptual approach fails to adequately highlight the maturity of certain innovative endeavors and effectively steer their development to higher TR levels. We will present an empirical case where the TRL approach presented a critical shortcoming in highlighting the true and effective readiness of a specific technological development and could not suggest the next natural step in ascending the maturity ladder. We will seek to generalize for the case of co-creation at large, analyze why co-creation may be poorly serviced by the current TRL model and suggest an amendment that would allow the observed shortcoming of the traditional TRL approach to be overcome and its use extended also in such co-creative settings.