High-level Knowledge Research Articles

Embedding Sustainability in Tourism Education: Bridging Curriculum Gaps for a Sustainable Future

The evolving challenges in the tourism industry, particularly those related to sustainability, demand significant revisions to current degree programs. This paper presents the design of a higher education curriculum for tourism studies at a Spanish university, responding to industry needs for broader, high-level knowledge and skills, with a strong emphasis on sustainability, digital transformation, and socially responsible practices. The curriculum prioritizes critical and strategic thinking alongside liberal reflection to empower students to lead in sustainable tourism development. Developed collaboratively with the entire tourism faculty, the curriculum design was informed by an extensive analysis of 105 international university courses, 204 job offers, feedback from 195 alumni, and ongoing consultations with professionals and community partners. Grounded in the concept of curriculum space and the philosophic practitioner framework, the curriculum integrates knowledge, soft skills, and attitudes to foster an innovative, sustainable learning ecosystem that prepares graduates to tackle the complexities of modern tourism with a focus on long-term environmental and social impacts.

Ability to Maintain and Improve Health and Socio-Demographic Correlates among Children in Albania.

Our aim was to assess the level and socio-demographic correlates of the ability to maintain health among children in Albania, a post-communist country in the Western Balkans. A cross-sectional study was conducted in Albania in September 2022. The study population consisted of a nationwide representative sample of 7,831 schoolchildren (≈54% girls) aged 12-15 years. A structured self-administered and anonymous questionnaire inquired about the children's attitudes toward health promotion (ability to maintain and improve health) and a range of key dimensions on knowledge and practices regarding healthcare services. Socio-demographic data were also collected. Binary logistic regression was used to assess the socio-demographic correlates of children's ability to maintain health. Overall, about 71% of schoolchildren exhibited the best attitudes toward health promotion (ability to maintain and improve health). In the multivariable adjusted logistic regression models, poorer attitudes toward health promotion (ability to maintain and improve health) were significantly related to older age (OR=1.2, 95% CI=1.0-1.3), middle/low maternal education (OR=1.2, 95%CI=1.1-1.3), poor/very poor economic situation (OR=1.5, 95%CI=1.2-1.8), lack of appropriate knowledge about rights to healthcare (OR=1.3, 95% CI=1.1-1.5), lack of knowledge about obtaining healthcare services (OR=1.7, 95%CI=1.4-2.2), and lack of consultations/visits with health professionals excluding vaccinations (OR=1.2, 95%CI=1.1-1.4). Almost 1/3rd of Albanian schoolchildren included in this study did not exhibit the best attitudes toward health promotion (ability to maintain and improve health). The best attitudes were related to a higher socioeconomic level and adequate knowledge and practices regarding healthcare services. There is thus a need to increase health literacy levels among children in order to ultimately induce sustainable healthy behavioural/lifestyle practices.

Development and evaluation of a blended learning training programme for pharmacy technicians’ continuing education

ObjectivesThe role of the pharmacy technician (PT) has expanded in recent years, requiring new competencies, better communications skills and high-level knowledge about drugs. The objective of this study is to...

The Assessment of Knowledge, Attitude, and Practice of Paracetamol and Ibuprofen Administration Among Saudi Parents in the Makkah Region.

Introduction Paracetamol and ibuprofen, widely used for pediatric fever and pain, are safe when administered correctly. However, the caregiver's lack of understanding poses risks such as overdose. Addressing knowledge gaps is crucial due to reported variations in over-the-counter medication practices. "Fever phobia" underscores parental anxiety, stressing the ongoing need for research in this healthcare domain. Methodology This is a descriptive cross-sectional design targeting Saudi parents and caregivers from the Makkah regionwho have children aged 0-10 years. Data was collected via a self-administered validated online questionnaire in the Arabic language using a convenient sampling technique. The data was cleaned in Excel and analyzed using SPSS version 29 (IBM Inc., Armonk, New York). Results Our study included 449 parents and caregivers in the Makkah Region, of whom 337 (75.1%) were female, 179 (39.9%) were aged 18-29, and 425 (94.7%) were Saudi nationals. Knowledge assessment revealed gaps; e.g., only 86 (26.6%) identified baby weight as a dosage factor. Attitudes varied, with 152 (47.1%) associating paracetamol/ibuprofen with liver harm. Logistic regression showed no significant predictors for high-level knowledge, positive attitudes, or good practices, except for gender-influencing good practices (p=0.035, aOR=1.839). Significantly, males exhibited better practices regarding using of paracetamol. Conclusion Our study highlights knowledge gaps among parents and caregivers in the Makkah Region regarding pediatric fever management with paracetamol and ibuprofen. Attitudes varied, and gender significantly influenced good practices, with males demonstrating better adherence to the proper practice of managing children using paracetamol and ibuprofen.

Reinforcement learning with predefined and inferred reward machines in stochastic games

This paper focuses on Multi-Agent Reinforcement Learning (MARL) in non-cooperative stochastic games, particularly addressing the challenge of task completion characterized by non-Markovian reward functions. We employ Reward Machines (RMs) to incorporate high-level task knowledge. Firstly, we introduce Q-learning with Reward Machines for Stochastic Games (QRM-SG), where RMs are predefined and available to agents. QRM-SG learns each agent’s best-response policy at Nash equilibrium by defining the Q-function in augmented state space that integrates the stochastic game and RM states. The Lemke-Howson method is utilized to compute the best-response policies for the stage game defined by the current Q-functions at each time step. Subsequently, we explore a more challenging scenario where RMs are unavailable and propose Multi-Agent Reinforcement learning with Concurrent High-level knowledge inference (MARCH). MARCH uses automata learning to learn RMs iteratively and combines this process with QRM-SG for learning the best-response policies. The RL episodes where the obtained rewards are inconsistent with the rewards from the current RMs trigger the inference of new RMs. We prove QRM-SG and MARCH converge to the best-response policies under certain conditions. Two scenarios are conducted to demonstrate the superior performance of QRM-SG and MARCH compared to baseline methods.

How to Drive Tourists’ Pro-Environmental Behavior: Interactive Effect of Benefit Appeals and Goal Framing

The drive to foster tourists’ pro-environmental behavior (TPEB) is a current research hotspot amidst the deteriorating ecological environment. However, existing studies have yet to reach definitive conclusions on how tourist destinations influence TPEB through different benefit appeals and goal framing. This paper investigates the interactive effects of these factors on TPEB, reporting on two experiments. Study 1 confirmed significant interactive effects of benefit appeals and goal framing on TPEB, wherein altruistic appeals and gain framing, as well as appeals to self-interest and loss framing, demonstrates matching effects. Furthermore, environmental awareness mediated these interactions. Study 2 established that tourists’ environmental knowledge moderates the interactive effects of goal framing and benefit appeals on TPEB. The moderating effect of high-level environmental knowledge is more pronounced, and tourists with higher environmental knowledge are more inclined to engage in TPEB compared with those with lower levels of environmental knowledge. The conclusions of this study not only enrich the research on influencing factors and mechanisms of TPEB but also offer practical insights for environmental management in tourist destinations.

HIV Knowledge and Its Associated Sociodemographic Factors among Female Sex Workers in Malaysia.

Female sex workers (FSWs) have a high risk of human immunodeficiency virus (HIV) infection. In spite of the alarming fact that a large proportion of FSWs does not have adequate HIV knowledge, the association between sociodemographic factors and HIV knowledge among FSWs have yet to be thoroughly explored in the context of Malaysia. The aims of this study are the following: i) to determine HIV knowledge and ii) to examine the associated factors of HIV knowledge. An observational cross-sectional study was conducted. Data from the Integrated Biological and Behavioral Surveillance Survey (IBBS) 2017 (n = 630) were used. The survey was carried out in all states in Malaysia and its duration was 4 months (from March 2017 to June 2017). Ordered probit regressions were utilised to shed light on the association between sociodemographic variables and levels of HIV knowledge. A large proportion of FSWs had middle-level HIV knowledge (44.1%). FSWs with tertiary-level education were 19.5% more likely to have high-level HIV knowledge compared to those without formal education. The probability of having low-level HIV knowledge was 6.8% lower among FSWs with monthly incomes of RM1,500-RM1,999 than those having incomes of ≤ RM499. Being single instead of married was associated with 7.6%-8% lower probabilities of having low- and middle-level HIV knowledge. Public health interventions to improve FSWs' HIV knowledge need to take into consideration the role of sociodemographic factors.

ASSESSMENT OF KNOWLEDGE OF AGRO-INPUT DEALERS GAINED THROUGH CERTIFICATE COURSE ON INSECTICIDE MANAGEMENT

Agriculture is the backbone of Indian economy and has a crucial role to play in the country's economic development. Agro-input dealers are an important mediator for transfer of technology among the farmer in rural area. Agro input dealers trained about insecticide management that significantly increased their knowledge. The study was conducted to assess the knowledge of Agro-input dealers gained through certificate course on insecticides management and to ascertain relationship between attributes of respondents and their knowledge gained through certificate course on insecticides management. The study was conducted in South Saurashtra Agro-climatic Zone in which total 405 agro-input dealers was selected for the study who were participated in the certificate course in the year 2021-22 at different centres of certificate course like Junagadh, Amreli, Khapat, Targhadiya, Dhari and Kodinar. The study revealed that after the completion of the certificate course, majority of agro-input dealers were from medium to high level knowledge group and course can be significantly increase the knowledge of agro-input dealers about insecticide management. The positive and significant relationship like education, numbers of university visit, numbers of training received and use of university technological product, revealed that these all variables had pivotal contribution in increase in knowledge of agro-input dealers. In addition to this, those agro-input dealers had contacted university by any means they get knowledge about insect pest management and insecticide management. The young farmers had more enthusiastic to acquire the knowledge about insecticide management. The regular and effective technology transfer of nearby recommended technological know-how, regular training should be imparted to young, educated and university contact farmers. May they were as change agents in the future.

Effective Algorithm for Biomedical Image Segmentation

Biomedical image segmentation plays an important role in quantitative analysis, clinical diagnosis, and medical manipulation. Objects in medical images have different scales, types, complex backgrounds, and similar tissue appearances, making information extraction challenging. To solve this problem, a module is proposed that takes into account the features of images, which will improve the biomedical image segmentation network FE-Net. An integral part of the FE-Net algorithm is the connection skipping mechanism, which ensures the connection and fusion of feature maps from different layers in the encoder and decoder. Features at the encoder level are combined with high-level semantic knowledge at the decoder level. The algorithm establishes connections between feature maps, which is used in medicine for image processing. The proposed method is tested on three public datasets: Kvasir-SEG, CVC-ClinicDB and 2018 Data Science Bowl. Based on the results of the study, it was found that FE-Net demonstrates better performance compared to other methods in terms of Intersection over Union and F1-score. The network under consideration copes more effectively with segmentation details and object boundaries, while maintaining high accuracy. The study was conducted jointly with the Department of Magnetic Resonance Imaging of the N. N. Alexandrov National Oncology Center. Access to the source code of the algorithm and additional technical details is available at https://github.com/tyjcbzd/FE-Net.

Listening to students: Beliefs and attitudes about active learning and effective lecture-style courses

Research shows that active-learning methods (e.g. discussion) are often superior to passive methods (e.g. lecture) with respect to learning outcomes. However, students sometimes report preferring and learning more in lecture-style classes compared to those in which they play an active role. These and related findings suggest students may lack awareness about the relative effectiveness of passive versus active teaching methods on their own learning. Here, we used a mixed methods approach to assess college students’ beliefs, attitudes, and feelings about lecture-style courses ( n = 246). One primary goal was to assess if, and to what extent, students would express an unprompted desire for interaction in lecture-style classes when reflecting, in an open-ended format, upon their real-world educational experiences. Respondents reported that the majority of their classes were lecture style, in accordance with previous research, and noted that they view lecture as a mostly effective form of pedagogy. While respondents described high-level knowledge and compelling delivery of the subject matter, effective class design and use of technology, and good stagecraft on behalf of the instructor as important aspects of effective lecture-style teaching, a substantial proportion of students mentioned interaction as the most important component of an effective lecture style class, suggesting that student preferences for passive classrooms is far from universal. Implications of these findings are discussed.

The use of antibiotics during pregnancy: A cross-sectional study of knowledge, attitude, and practices among antenatal care attendees in Northern Ghana.

The promotion of rational use of antibiotics among pregnant women is eminent not only for the risk of teratogenicity in the developing fetus but also the risk of drug resistance with its concomitant high cost of health care. Studies on antibiotic self-medication among pregnant women in Northern Ghana are rare. Improving the knowledge and awareness among the vulnerable groups about the appropriate use of antibiotics can help in limiting the antibiotic resistance menace. We, therefore, conducted this study to assess the knowledge, attitude, and practice (KAP) toward antibiotic use among pregnant women attending an antenatal clinic at a primary health care in Tolon, Northern Region, Ghana. We conducted a cross-sectional study using an interviewer-administered questionnaire to assess the KAP of 702 pregnant women on antibiotic use. This study was conducted in the Tolon Health Center (THC) from March 2021 and ended in October 2021. In this study, 55.6% of pregnant women had good knowledge and 45.3% of them had engaged in self-medication with antibiotics while pregnant. There were statistically significant associations between participants' background and obstetric characteristics and knowledge of antibiotic use and antibiotic resistance, except for age, marital status, and parity. Also, there was a significant association between pregnant women's knowledge and self-medication or over-the-counter purchase of antibiotics. We concluded that higher education level, monthly income, good practice, and good knowledge were significantly associated with a reduced likelihood of self-medication with antibiotics. A well-structured education that could be easily accepted and understood by pregnant women on the risks of antibiotic self-medication should be included in the routine education at the antenatal clinics.

Conditional generative adversarial networks for image-based sunlight analysis of residential blocks

Sunlight simulation is often used to analyze whether buildings receive sufficient sunlight in complex design cases. However, it is time-consuming to establish geometrical and physical models, and non-negligible computation time (from a few seconds to minutes) leads to high time cost of iterative design optimization. Therefore, this study builds surrogate models to instantaneously detect problematic buildings (those receiving insufficient sunlight) using images of residential blocks. Different conditional generative adversarial networks (CGANs), including Pix2Pix and conditional attention GAN (CAGAN), are employed to train surrogate models. The attention mechanisms and the multi-scale vision give CAGAN an edge in capturing the global structure of buildings and learning high-level knowledge related to the occlusion relationships between buildings. In addition, this study also proposes a stepwise strategy to construct composite surrogate models. The first sub-model provides more informative images to help the second sub-model learn the features of problematic buildings. This study focuses on evaluating the models based on the detection performance in addition to image structural similarity and pixel similarity, which have been used in most studies. The CAGAN models outperform the Pix2Pix models in the average detection accuracy (ADA) and the 100 % successful detection probability (SDP) on the same dataset. Moreover, the stepwise strategy increases the ADA and SDP to 89.88 % and 71.07 %, respectively. The proposed methods can be extended to other urban environmental performance diagnoses, such as thermal comfort or the wind environment.

The Link between Family Financial Socialization in Adulthood and Investment Literacy of P2P Investors

AbstractThis paper examines how family financial socialization in adulthood is linked to the development of investment literacy among individual family members within the context of innovative financial services, specifically peer-to-peer (P2P) lending. Our findings revealed that P2P lending investors engage in a moderate level family financial socialization suggesting that family, as a key financial socialization agent in childhood and adolescence, maintains its role in adulthood. Additionally, such investors possess a high-level investment knowledge, skills, and attitudes. Explicit family financial socialization has a significant and positive effect on the individuals’ investment knowledge, skills, and attitudes, while the effect of implicit financial socialization is significant but negative for knowledge and attitudes. Such findings suggest that family discussion among adult members result in higher, while observations of family members’ investment behavior led to lower investment literacy. Our study found no significant moderating effect of the strength of social ties indicating that dynamics of family relations neither strengthen nor weaken proximal socialization outcomes. The analysis of differences across demographic groups unveiled statistically significant distinctions concerning respondents’ gender, income, and education. These results provide important insights for stakeholders, underscoring the significant role family socialization in adulthood plays in shaping individuals’ investment literacy, particularly of those investing on P2P lending platforms.

Automatic analysis of pottery sherds based on structure from motion scanning: The case of the Phoenician carinated-shoulder amphorae from Tell el-Burak (Lebanon)

Over the last few years, significant interest has been addressed in developing computer-based methods to document and analyze fragments of ceramics sherds in archaeology. This is because traditional manual processes do not allow for an objective, repeatable, and reproducible analysis of the large quantities of material needed to fully understand and explain human practices in various cultural contexts, such as the economy, daily life, and the material expression of religious beliefs.In that context, this paper proposes a fully digital methodology resulting from the constitution of an international research group coming from different scientific backgrounds: archaeologists with specific skills and experience in fast 3D geometry acquisition methods and researchers who developed and published the only available computer-based process for recognizing the geometric and morphological sherds features analyzed by archaeologists. The proposed methodology consists of two main parts: 1. 3D acquisition of sherds with the construction of the discrete 3D manifold model based on the Structure for Motion technologies; 2. recognition, segmentation, and dimensional characterization of morphological and geometrical features based on the codification and algorithmic implementation of the knowledge used by the archeologists in the traditional method. The method was applied to analyze a set of 133 sherds excavated at Tell el-Burak (Lebanon) to obtain, through the analysis of the namely Phoenician carinated-shoulder amphorae, new insights into the economic organization of the Phoenician homeland. The method demonstrated the potential for objectively, repeatedly, and reproducibly analyzing large quantities of sherds. Furthermore, it allowed studying sherds by generating new high-level knowledge from those acquired from 3D models; in particular, this paper introduces new morphological features that help the archaeologist classify fragments from an analysis of the rim's shape.

A scalable multi-density clustering approach to detect city hotspots in a smart city

In the field of Smart City applications, the analysis of urban data to detect city hotspots, i.e., regions where urban events (such as pollution peaks, virus infections, traffic spikes, and crimes) occur at a higher density than in the rest of the dataset, is becoming a common task. The detection of such hotspots can serve as a valuable organizational technique for framing detailed information about a metropolitan area, providing high-level spatial knowledge for planners, scientists, and policymakers. From the algorithmic viewpoint, classic density-based clustering algorithms are very effective in discovering hotspots characterized by homogeneous density; however, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are characterized by areas with significantly variable densities, multi-density clustering approaches are more effective in discovering city hotspots. Moreover, the growing volumes of data collected in urban environments require the development of parallel approaches, in order to take advantage of scalable executions offered by Edge and Cloud environments. This paper describes the design and implementation of a parallel multi-density clustering algorithm aimed at analyzing high volumes of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time, speedup, and efficiency.

Video Event Extraction with Multi-View Interaction Knowledge Distillation

Video event extraction (VEE) aims to extract key events and generate the event arguments for their semantic roles from the video. Despite promising results have been achieved by existing methods, they still lack an elaborate learning strategy to adequately consider: (1) inter-object interaction, which reflects the relation between objects; (2) inter-modality interaction, which aligns the features from text and video modality. In this paper, we propose a Multi-view Interaction with knowledge Distillation (MID) framework to solve the above problems with the Knowledge Distillation (KD) mechanism. Specifically, we propose the self-Relational KD (self-RKD) to enhance the inter-object interaction, where the relation between objects is measured by distance metric, and the high-level relational knowledge from the deeper layer is taken as the guidance for boosting the shallow layer in the video encoder. Meanwhile, to improve the inter-modality interaction, the Layer-to-layer KD (LKD) is proposed, which integrates additional cross-modal supervisions (i.e., the results of cross-attention) with the textual supervising signal for training each transformer decoder layer. Extensive experiments show that without any additional parameters, MID achieves the state-of-the-art performance compared to other strong methods in VEE.

Impact of Teacher Professional Development on Student Learning at Secondary Level

Abstract: In this paper we discuss about a study on impact of teacher professional development on student learning at secondary level. In a world where teachers are required to become ‘high-level knowledge workers’, the emergence of the school as learning organization is vital. In this context, the aim of this systematic review was to explore what comprises effective professional development for educators: i.e. what forms of teacher professional development definitively lead to positive outcomes for students? The NEP-2020 provides for a comprehensive, sustainable and reformative roadmap for a paradigm shift in the entire education system in the country, and is culturally grounded, is geared toward quality transformation, and is internationally competitive. Teacher professional development involves a continuous process of reflection, learning and further a teacher’s knowledge and skills, leading to enhanced teaching practices that positively impact on student’s learning. A teacher training programme provides teachers with skills and current pedagogy practices to help them connect with, manage, and educate their students in a way that ensures all students are learning and benefiting. A teacher professional development provides teachers with technical help to educate them and further train them in specialized areas. It also provides them with an in-depth understanding of how teacher training affects student learning. When executed properly and with the appropriate material, teacher development programmes have the potential to develop teachers to the point where they can favorably affect students not just academically but also socially. Education is the cornerstone of societal development, and at the heart of education is the teacher-student dynamic. The role of teachers extends far beyond imparting knowledge; they shape young minds, inspire curiosity, and foster a love for learning. Teachers are an important element of the education system and play an important role in the effective development and implementation of educational policies. Teacher training is not a one-time event; it is an ongoing process that encourages continuous professional development. Educators who engage in regular training sessions stay abreast of the latest educational research, methodologies, and best practices. This commitment to lifelong learning ensures that teachers remain effective and adaptable throughout their careers, positively impacting student outcomes. Teachers may have a significant influence on students and push them to stay on track during their formative years. Students who see their teachers as role models achieve extraordinary results.

Historic British Royal Memes

Historic British Royal Memes

Geoarchaeology of earthen Cypriot architecture: Integrated analyses of mudbricks from Middle Bronze Age Cyprus

Mudbricks constitute an integral part of prehistoric architecture in Cyprus. As a key material of building construction, mudbricks possess more than just structural information and environmental data; they represent optimal indicators to identify the production choices as means for social representation and communication. Results obtained from the multi-proxy analysis of mudbrick materials recovered and sampled at the Middle Bronze Age site of Erimi-Laonin tou Porakou (c. 1950–1650 BCE) are presented in this paper as a geoarchaeological examination of prehistoric earthen architecture in Cyprus. The analysis revises previously developed and published results (see Amadio, 2017) and integrates them on the basis of newly acquired macro and micro evidence. For the present study, mudbricks were preliminary examined in the field and further analysed by high-resolution micromorphological, spectroscopic and geochemical analyses with the aim of generating new interdisciplinary data to study aspects of procurement strategies, and technological choices operated by local workers at Erimi. Results indicate that materials were sourced locally according to their easy accessibility, however, the expert selection of sediments and tempers in the mixtures points toward an organised craft production. The identification of proper recipes in mudbricks and the consistency in shape and size observed in the intact materials recovered at the site, as well as the accomplishment of a large-scale project such as the construction of the productive area of the settlement -the workshop complex- suggest the existence of cooperative forms of labour in the construction practices, which were possibly organised at the supra-household level and were based on high-level technological knowledge.

Knowing how to learn: development and validation of the strategy knowledge test for self-regulated learning (SKT-SRL) for college students

Self-regulated learning (SRL) encompasses cognitive, metacognitive, and motivational learning strategies and is highly relevant for academic achievement. Although students have mostly acquired high-level SRL strategy knowledge by the time they reach college, they often show deficiencies in their application of SRL strategies. In order to investigate the gap between SRL strategy knowledge and its application in more depth, it is necessary to develop instruments that assess conditional SRL strategy knowledge for the whole learning process with sufficient validity and reliability. Therefore, based on four distinct studies conducted in Germany, the development and validation of a new Strategy Knowledge Test for Self-Regulated Learning (SKT-SRL) for college students are described. Study 1 describes the development of the test and the results of expert ratings that speak in favour of the test’s content validity. Study 2, the pilot study, was undertaken to gain insight into the psychometric characteristics of the SKT-SRL, examine its relationship to other SRL assessment methods and academic achievement, and reach a sense of its validity. Studies 3 and 4 present the results of two validation studies for the SKT-SRL; they investigated its psychometric characteristics, convergent validity, factorial structure, test–retest reliability, and its relationship to academic achievement as well as other study-relevant factors. Overall, the results are promising and the SKT-SRL mostly showed sufficiently valid assessments. To conclude, the SKT-SRL is a useful tool for analysing conditional SRL strategy knowledge in college students and can be used for further research on the relationship between SRL strategy knowledge and strategy usage.