Search Results (266)

Background & Objectives: Correct nutrition during pregnancy is key to guaranteeing success at this stage of a woman’s life, and nutritional education is the fundamental tool for achieving this. Studies carried out in different countries indicate that pregnant women do not comply with dietary and nutritional recommendations. Given the lack of evidence available in Spain and the importance of this knowledge to be able to assess the need for nutritional intervention in this group, the aim of this study focused on the current status of the issue in Spain: the quality of the diet of Spanish pregnant women and its conditioning factors. Methods: Two representative regions of the country were selected, one located in the north of Spain (Burgos) and the other in the south (Granada), and a descriptive, cross-sectional observational study (sample size: 771) was carried out using a questionnaire administered at the University Hospital of Burgos and the Hospital Clínico San Cecilio in Granada, which had previously been subjected to a process of evaluation by expert judgement. Results: Pregnant women presented an adequate diet quality (8.0 ± 2.0), according to the questionnaire used, despite their poor knowledge of food and nutrition (4.9 ± 1.6 out of 10). However, deficiencies were detected in the consumption of very interesting food groups from a nutritional point of view, such as legumes, nuts and fish (just 29.4%, 37.6% and 24.8% of the pregnant women met the recommendations, respectively) and insufficient physical exercise. The eating habits of pregnant women depend on their age, their country of origin, their level of education, their pre-pregnancy BMI, the knowledge acquired during pregnancy and the degree to which they put into practice the advice received from their midwife. Most pregnant women do not change their habits during pregnancy, although there are positive trends in this respect. Conclusion: The quality of the diet of the Spanish pregnant women surveyed, and their level of physical activity, could be improved by enhancing the nutritional education they receive during this stage of life. The role of the dietician in this respect, as part of multidisciplinary teams, should be the basis for future research. Full article

In the last few decades, the promotion of active transport has been a viable solution recommended by transportation researchers, urban planners, and policymakers to reduce traffic congestion and improve public health in cities. To encourage active transport, it is important for cities to provide safe and accessible infrastructure for pedestrians and cyclists, as well as incentives for individuals to choose active modes of transportation over private vehicles. In this research, we focused on the suburb of Point Cook, located within the City of Wyndham in Melbourne’s west, owing to its rising human population and private vehicle ownership. The primary aim of this research is to examine the barriers in the interconnectivity of active transport networks for pedestrians and cyclists and to determine the segments of the transportation network that are not accessible to Point Cook residents. Our methodology is enshrined in the use of Social Pinpoint, which is an online interactive survey platform, and ground surveys (face-to-face interviews). In our assessment of the suburb of Point Cook, we utilised the concept of 20-min neighbourhoods to evaluate the accessibility of many important places within an 800-metre walking distance from residents’ homes. Based on our online interactive survey findings, approximately one-third of the individuals engaged in regular walking, with a frequency ranging from once a day to once every two days. One-third of the participants engaged in walking trips once or twice a week, whereas the remaining two-thirds conducted walking trips less frequently than once a week. Almost 89% of the participants expressed varying levels of interest in increasing their walking frequency. The findings showed that improving pedestrian and cycling networks that are easily accessible, well-integrated, inclusive, and safe is a prerequisite for achieving active transport and create neighbourhoods in which everything is accessible within a 20-min walking distance. Full article

The objective of cross-city recommendation is to suggest points-of-interest (POI) in the target city that may be of interest to users, based on their check-in records from their source city. Although significant progress has been made in studying user preference transfers, there is a lack of research focusing on personalized user preference transfers. Furthermore, the mining of user preferences from the source city is impacted by errors and missing information. To address these challenges, this paper proposes a Check-In Heterogeneous Hypergraph and Personalized Preference Transfers for Cross-City POI Recommendation Method (CHHPPT). Firstly, a check-in heterogeneous hypergraph network is introduced in the user source city preference-mining module. This network, through Heterogeneous Hypergraph Embeddings (HHE), captures user preferences in the source city, thereby mitigating the impact of errors and missing information on user preference. Subsequently, in the user-personalized preference transfer module, a user’s transferable features are obtained through a POI aggregation network. These features are then combined with a meta-network and transfer networks to achieve user-personalized preference transfer. Finally, in the target city point-of-interest recommendation module, a POI-geographical graph is constructed using the geographical information of POI. This graph, in conjunction with category information, yields a joint embedding representation. The final recommendation is achieved by integrating the user-personalized preference transfer embeddings with the target city’s POI embeddings. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of CHHPPT in cross-city recommendation tasks. Full article

Given that spatial accessibility to tourist resources is crucial for tourism development, this study analyzes the role of the distance between tourists and archaeological sites in the Dobrogea region of Romania. This study highlights the impact of road distance in the valorization of the main archaeological sites in the chosen area, the results obtained providing information on the main parameters of archaeological resources in achieving tourism success. These data will be important clues in the future design of plans for the valorization of those archaeological sites not yet valorized from the tourist point of view. Spatial data modeling was performed using specific Geographic Information Systems tools, which allowed us to extract the necessary information. By corroborating the results of the geospatial analysis with the statistical ones, we were able to draw conclusions regarding the tourist behavior in the region and the decision factors of tourists in visiting the ancient or medieval ruins in Dobrogea. Our analysis shows a significant correlation between the road accessibility of archaeological sites and their tourist attractiveness, with a particular influence of their location in relation to the main tourist areas of interest in the region. The study area has a rich history that has left behind a high density of ancient fortresses, citadels, and cities, which are relatively underutilized for tourism. Heritage tourism has thus lagged behind coastal tourism, which attracts the highest number of tourists in the country. A better knowledge of the factors that favor the development of heritage tourism is needed in order to expand the region’s tourist offer. Our recommendations aim to improve the accessibility and attractiveness of these archaeological sites by investing in essential infrastructure, developing sustainable transportation policies and specific tourism facilities, and implementing a regional strategy to enhance and protect them, which will ultimately increase their contribution to local economies. Full article

This research synthesis, highlighting the middle level curriculum, was created as part of a working group within the American Education Research Association (AERA) Middle level Education Research Special Interest Group (MLER SIG) to advance middle level education research. The literature review that informed this research synthesis included middle level-focused, peer-reviewed journal articles from 2016 to the present. Synthesis of the literature and iterative analysis led to organizing a middle level curriculum framework to inform middle level researchers, which included five focused areas for inquiry: (a) the curriculum and equity of experience and opportunity; (b) stakeholder power and the curriculum (development, implementation, and accountability); (c) goals and purposes for the curriculum; (d) teacher learning, roles, and enactment related to the curriculum; and (e) young adolescent well-being and experiences with the curriculum. Twenty-six research questions were developed to support new research in middle level curriculum; each question was cross-referenced with the five categories in the middle level curriculum framework to create a robust starting point for research questions. Recommendations for middle level research include (a) the need for more longitudinal research studies focusing on varying aspects of middle level education, (b) the need for more large-scale research studies examining the same context (e.g., school) or multiple contexts (e.g., school districts) over time, and (c) the need for the MLER SIG to initiate and support research studies addressing one or more aspects of this research agenda and include large-scale data collection and potentially external funding. Full article

Background/Objectives: There is potential utility and increasing interest in engaging professionals in non-traditional vaccination settings to participate in efforts to reduce human papillomavirus (HPV)-related cancer. This study assessed the impact of a multi-disciplinary HPV educational intervention on oral health care professionals’ perceived role, comfort level, and scope of practice in HPV-related cancer prevention efforts. Methods: The virtual educational intervention was provided by a multi-disciplinary panel of experts. Seventy-three oral health care professionals attended the educational intervention and completed a questionnaire at three time points (pre-session, immediate post-session, and at the 1-month follow-up). Data were analyzed using Friedman’s ANOVA and post-hoc analyses. Results: Respondent’s median belief that it is the role of an oral health professional to recommend the HPV vaccine increased from pre-session (Median = 3.0, IQR = 3.0–4.0) to immediate post-session (median = 4.5, IQR = 4.0–5.0), and this increase was maintained 1 month after the session (median = 4.0, IQR = 4.0–4.5; p < 0.001). Additionally, respondent’s belief that they were up-to-date on the latest guidelines for HPV vaccination also increased from pre-session to immediate post-session (p < 0.05), and this increase was maintained 1 month after the session (pre-session median = 2.0, IQR = 2.0–3.0 vs. 1-month post-session median = 4.0, IQR = 4.0–5.0; p < 0.005). Conclusions: The multi-disciplinary HPV educational intervention was well-received by oral health professionals. Data suggest the intervention had a lasting impact on their beliefs about their role, comfort level, and scope of practice relating to HPV cancer prevention. More research needs to be conducted to better understand how obstetrician-gynecologists, other obstetric care providers, and oral health communities can support each other in promoting HPV-related cancer prevention. Full article

The utilization of location-based social networks to provide point-of-interest (POI) recommendation services has been the subject of extensive research in recent years. Various factors that can enhance the precision of POI recommendations were examined in previous studies. However, the factors of a user, including the location and time, were not considered. In this paper, we proposed a POI recommendation scheme in which user activity patterns and the similarity of categories are considered. The proposed scheme is used to organize users based on the activity level and to take into account the characteristics of both the user and location. Furthermore, it provides personalized recommendations by considering the category similarity, time, and location data that were collected from users. We evaluated the performance of the proposed scheme and compared it with that of a currently used scheme. The proposed scheme exhibits precision that is approximately 16% greater than that of the existing scheme. Full article

Next point-of-interest (POI) recommendation provides users with location suggestions that they may be interested in, allowing them to explore their surroundings. Existing sequence-based or graph-based POI recommendation methods have matured in capturing spatiotemporal information; however, POI recommendation methods based on large language models (LLMs) focus more on capturing sequential transition relationships. This raises an unexplored challenge: how to leverage LLMs to better capture geographic contextual information. To address this, we propose interpretable embeddings for next point-of-interest recommendation via large language model question–answering, named QA-POI, which transforms the POI recommendation task into obtaining interpretable embeddings via LLM prompts, followed by lightweight MLP fine-tuning. We introduce question–answer embeddings, which are generated by asking LLMs yes/no questions about the user’s trajectory sequence. By asking spatiotemporal questions about the trajectory sequence, we aim to extract as much spatiotemporal information from the LLM as possible. During training, QA-POI iteratively selects the most valuable subset of potential questions from a set of questions to prompt the LLM for the next POI recommendation. It is then fine-tuned for the next POI recommendation task using a lightweight Multi-Layer Perceptron (MLP). Extensive experiments on two datasets demonstrate the effectiveness of our approach. Full article

As to the problems in current tourism recommendation, this paper proposes a tourism recommendation algorithm based on the mobile ICV service platform. Firstly, the ICV service system for the Point of Interest (POI) searching and route recommendation is designed. Secondly, the recommendation service model is set up from two aspects, namely the tourism POI clustering algorithm and the tourism POI searching and route recommendation algorithm. In the aspect of symmetrical-based matching features, the clustered POIs are matched with the tourists’ interests, and the POIs in the neighborhood of the ICV dynamic locations are searched. Then, a POI recommendation algorithm based on the tourists’ interests is constructed, and the POIs that best match the symmetrical interests of the tourists within the dynamic buffer zones of ICV are confirmed. Based on the recommended POIs, the ICV guidance route algorithm is constructed. The experiment verifies the advantages of the proposed algorithm on the aspect of the POI matching tourists’ interests, algorithm stability, traveling time cost, traveling distance cost and computational complexity. As to the iterative sum and the iterative sum average of the POI matching function values, the proposed algorithm has a performance improvement of at least 20.2% and a stability improvement of at least 20.5% compared to the randomly selected POIs in matching tourists’ interests. As to the cost of the guidance routes, the proposed algorithm reduces the average cost by 19.6% compared to the other suboptimal routes. Compared with the control group algorithms, the proposed algorithm is superior in terms of route cost, with an average cost reduction of 13.8% for the output routes compared to the control group. Also, the proposed algorithm is superior in terms of route cost compared to the control group recommendation algorithms, with an average cost reduction of 11.2%. Full article

Tourism has become one of the world’s largest service industries. Due to the rapid development of social media, more people like self-guided tours than package itineraries planned by travel agencies. Therefore, how to develop itinerary recommendation systems that can provide practical tour suggestions for tourists has become an important research topic. This study proposes a novel tour recommendation system that considers the implicit and dynamic information of Point-of-Interest (POI). Our approach is based on users’ photo information uploaded to social media in various tourist attractions. For each check-in record, we will find the POI closest to the user’s check-in Global Positioning System (GPS) location and consider the POI as the one they want to visit. Instead of using explicit information such as categories to represent POIs, this research uses the implicit feature extracted from the textual descriptions of POIs. Textual description for a POI contains rich and potential information describing the POI’s type, facilities, or activities, which makes it more suitable to represent a POI. In addition, this study considers visiting sequences when evaluating user similarity during clustering so that tourists in each sub-group hold higher behavior similarity. Next, the Non-negative Matrix Factorization (NMF) dynamically derives the staying time for different users, time slots, and POIs. Finally, a personalized itinerary algorithm is developed that considers user preference and dynamic staying time. The system will recommend the itinerary with the highest score and the longest remaining time. A set of experiments indicates that the proposed recommendation system outperforms state-of-the-art next POI recommendation methods regarding four commonly used evaluation metrics. Full article

Background/Objectives: Infrared thermography (IRT) is an imaging technique used in clinical practice to detect changes in skin temperature caused by several dysfunctions, including breast cancer-related lymphedema (BCRL). Thus, the present study aimed to assess the reproducibility and accuracy of IRT in diagnosing BCRL. Methods: This cross-sectional study included participants who underwent a unilateral mastectomy and used indirect volumetry for lymphedema detection. IRT analysis was recorded in four positions, analyzing maximum, mean, and minimum temperatures, as well as the temperature differences between the upper limbs. The analysis encompassed reliability, agreement, accuracy, and the establishment of cut-off points for sensitivity and specificity. A total of 88 upper limbs were included; 176 thermograms were captured, and 1056 regions of interest were analyzed. Results: IRT presented excellent intra- and inter-rater reproducibility and reliability with excellent intraclass correlation coefficient values (0.99 to 1.00). In addition, this assessment reached a sensitivity of 85% and a specificity of 56%; the cut-off point considered a temperature difference of −0.45 °C. Conclusions: IRT was a reliable and reproducible assessment, and the temperature difference between the upper limbs evidenced moderate accuracy. Thus, IRT is recommended as a complementary technique for detecting BCRL. Full article

Animal venom has been gaining traction as a potential source of therapeutics for various diseases. Spiders encompass a wide variety of venom-producing species, of which tarantulas of the family Theraphosidae are widely known across the globe. Research towards tarantula venom therapeutics has led to its potential application as antinociceptives. Death receptors are cellular receptors that induce apoptosis—the body’s natural suicide mechanism—to destroy malfunctioning cells. These are particularly of interest in cancer research, as this mechanism is tampered with, resulting in cancer cell proliferation. In this study, the viability of venom toxins from the Theraphosidae family of spiders to induce apoptosis by binding to human death receptors is investigated by carrying out anti-cancer screening, molecular docking, ADMET evaluation, then molecular dynamics and thermodynamic analysis twice, first to ascertain the best receptor–peptide systems per receptor, and secondly to more comprehensively describe binding stability and thermodynamics. Results point to favorable receptor–peptide interactions due to similarities in equilibrium behavior with the death ligand–death receptor systems, along with favorable end-state binding energies and ADMET analysis results. Further inquiry is recommended to assess the real-life efficacy and viability of theraphotoxins as apoptosis therapeutics and further improve on their ability to induce apoptosis. Full article

Methods for determining the characteristics of self-acting (aerodynamic) gas bearings have been developed for many years, but many researchers and engineers still question how sophisticated a model of such bearings should be to obtain reliable results. This is the subject of this article, which presents a numerical analysis of aerodynamic gas bearings using two alternative methods: a specialized program based on the finite difference method, and a universal CFD program using the finite volume method. Gas bearings with a nominal diameter of 49 mm, designed for a 10 kW turbogenerator operating at a rotational speed of 40,000 rpm, are analyzed. The vapor of the low-boiling medium, designated HFE-7100, is used as the bearing lubricant. The calculations focus on determining the position of the bearing journal where the bearing achieved the required load capacity and checking the bearing characteristics beyond the nominal operating point. The most important results obtained by the two independent methods are compared, and recommendations are made for those interested in the numerical analysis of self-acting gas bearings. Full article

In recent years, graph-based learning methods have gained significant traction in point-of-interest (POI) recommendation systems due to their strong generalization capabilities. These approaches commonly transform user check-in records into graph-structured data and leverage graph neural networks (GNNs) to model the representations of both POIs and users. Despite their effectiveness, GNNs face inherent limitations in message passing, which can impede the deep extraction of meaningful representations from the graph structure. To mitigate this challenge, we introduce a novel framework, Multi-view Contextual Graphs via Convolutional Neural Networks for Point-of-Interest Recommendation (MCGRec). The MCGRec framework consists of three primary components. Firstly, it employs a personalized PageRank (PPR) sampling technique based on super nodes to transform the graph-structured data into a grid-like feature matrix. This step is crucial as it prepares the data for subsequent processing by convolutional neural networks (CNNs), which are adept at extracting spatial features from grid-like structures. Secondly, a CNN is utilized to extract the representations of POIs from the constructed feature matrix. The usage of CNNs enables the capture of local patterns and hierarchical features within the data, which are essential for accurate POI representation. Lastly, MCGRec incorporates a novel approach for estimating user preferences that integrates both geographical and temporal factors, thereby providing a more comprehensive model of users’ behaviors. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on real-world datasets. Our results demonstrate that MCGRec outperforms state-of-the-art POI recommendation methods, showcasing its superiority in terms of recommendation accuracy. Full article

This research leverages bibliometric methodologies, enhanced by the visual analytics capabilities of CiteSpace, to meticulously examine the evolution and current trends in energy trading within power markets, analyzing 642 scholarly articles from the Web of Science Core Collection spanning from 1996 to 2023. The study aims to illuminate the prevailing research landscape, growth patterns, and future directions in energy trading dynamics. Key findings include: (1) A noticeable escalation in the volume of publications, especially from 2021 to 2023, indicating a burgeoning interest and rapid evolution in this research area; (2) The author and institutional collaboration networks are in a nascent stage, with a predominantly China-centric international collaboration pattern, including significant partnerships with the United States, Australia, and the United Kingdom; (3) The focal points of research are centered around themes such as “energy management”, “demand-side innovation”, “decentralized energy trading”, and “strategic optimization”, covering areas such as intelligent grid technologies, energy market dynamics, and sustainable energy solutions. The study recommends enhancing collaborative networks, fusing technological and strategic dimensions in research, increasing focus and funding for emerging technologies, and promoting wider international and cross-disciplinary collaborations to enrich the understanding of energy trading dynamics in the context of electricity markets. Full article