Scientific Recommendation Research Articles

Optic nerve sheath diameters in nontraumatic brain injury: A scoping review and role in the intensive care unit.

Neuromonitoring in medical intensive care units is challenging as most patients are unfit for invasive intracranial pressure (ICP) modalities or unstable to transport for imaging. Ultrasonography-based optic nerve sheath diameter (ONSD) is an attractive option as it is reliable, repeatable and easily performed at the bedside. It has been sufficiently validated in traumatic brain injury (TBI) to be incorporated into the guidelines. However, currently the data for non-TBI patients is inconsistent for a scientific recommendation to be made. To compile the existing evidence for understanding the scope of ONSD in measuring ICP in adult non-traumatic neuro-critical patients. PubMed, Google Scholar and research citation analysis databases were searched for studies in adult patients with non-traumatic causes of raised ICP. Studies from 2010 to 2024 in English languages were included. We found 37 articles relevant to our search. The cutoff for ONSD in predicting ICP varied from 4.1 to 6.3 mm. Most of the articles used cerebrospinal fluid opening pressure followed by raised ICP on computed tomography/magnetic resonance imaging as the comparator parameter. ONSD was also found to be a reliable outcome measure in cases of acute ischaemic stroke, intracerebral bleeding and intracranial infection. However, ONSD is of doubtful utility in septic metabolic encephalopathy, dysnatremias and aneurysmal subarachnoid haemorrhage. ONSD is a useful tool for the diagnosis of raised ICP in non-traumatic neuro-critically ill patients and may also have a role in the prognostication of a subset of patients.

S3PaR: Section-based Sequential Scientific Paper Recommendation for paper writing assistance

A scientific paper recommender system (RS) is very helpful for literature searching in that it (1) helps novice researchers explore their own field and (2) helps experienced researchers explore new fields outside their area of expertise. However, existing RSs usually recommend relevant papers based on users’ static interests, i.e., papers they cited in their past publication(s) or reading histories. In this paper, we propose a novel recommendation task based on users’ dynamic interests during their paper-writing activity. This dynamism is revealed in (for example) the topic shift while writing the Introduction vs. Related Works section. In solving this task, we developed a new pipeline called “Section-based Sequential Scientific Paper Recommendation (S3PaR)”, which recommends papers based on the context of the given user’s currently written paper section. Our experiments demonstrate that this unique task and our proposed pipeline outperform existing standard RS baselines.

Prevalence of elevated lipoprotein(a) in cardiac rehabilitation patients - results from a large-scale multicentre registry in Germany.

Lipoprotein(a) (Lp(a)) is an independent risk factor for myocardial infarction and aortic valve stenosis. European guidelines recommend assessing it at least once in a lifetime, particularly in premature atherosclerotic heart disease. A non-interventional registry was conducted at MEDIAN rehabilitation facilities in Germany to assess the frequency of Lp(a) testing in referring acute care hospitals and the prevalence of elevated Lp(a) levels in aortic valve stenosis or premature myocardial infarction. All consecutive patients referred after coronary intervention or aortic valve surgery were included in four cohorts: aortic valve intervention (cohort 1), current/previous myocardial infarction at < 60 years of age (cohorts 2a/2b), and myocardial infarction at ≥ 60 years of age (control). The analysis included 3393 patient records (cohort 1, n = 1063; cohort 2a, n = 1351; cohort 2b, n = 381; control, n = 598). Lp(a) had been determined at the referring hospital in 0.19% (cohort 1), 4.96% (cohort 2a), 2.36% (cohort 2b), and 2.01% (control) of patients. Lp(a) levels were > 50 mg/dL or > 125 nmol/L in 28.79% (cohort 1), 29.90% (cohort 2a), and 36.48% (cohort 2b; p < 0.001) compared to 24.25% (control). Family history of premature cardiovascular disease was reported in 13.45% (cohort 1), 38.56% (cohort 2a), and 32.81% (cohort 2b) compared to 17.89% (control; p < 0.05 for each comparison). Lp(a) had been rarely assessed in acute management of aortic valve stenosis or premature myocardial infarction despite expanding scientific evidence and guideline recommendation. Given the above-average incidence of elevated Lp(a) levels, awareness for Lp(a) has to increase substantially to better identify and manage high-risk patients.

Research on MOOC Curriculum Recommendation Model of Higher Vocational English based on Improved Intensive Learning Network

With the development and maturity of the Internet education industry, more and more vocational colleges have opened English teaching courses based on massive open online courses courses. However, there are many English-related courses on the massive open online courses course platform, and the use of scientific recommendation models can improve the teaching quality of such courses. Therefore, this research attempts to design two improved attention mechanisms and user-based embedded expression using meta-path technology. At the same time, these two are combined with reinforcement learning technology to design an improved massive open online courses English course recommendation model. The test results show that the hit rate of the model designed in this study is 89.84%, 74.28%, 70.81% and 71.35% respectively when the rank number is 20 and the parameter is 10. At this time, the cumulative income of normalized discount is 48.24%, 34.58%, 25.96% and 28.69% respectively. However, when the number of calculated samples reaches the maximum value of 1158609, the calculation time of the improved reinforcement learning recommendation model is 1867 seconds, which is also higher than the comparison model. The experimental results show that the curriculum recommendation accuracy of the massive open online courses recommendation model designed in this study is higher and the recommendation results are more reasonable. The results of this research have a certain application potential in the field of the construction of online education in colleges and universities.

The level of farmers Application of scientific Recommendation Related to the use of wells in Al-baaj District/Nineveh governorate –Iraq

This study aimed to determine the level of farmer application of scientific recommendation related to the use, design ,Protection and follow-up of wells and Its relationship with some factors in Al-baaj district, the questioner form was used to collected data, the research sample included 124 farmers were chosen in a relatively random method , then data collection tools were prepared to achieve the research objectives which consisted of two parts: the first was related the personal factors as well as the second part included two fields : the design, protection from pollution and follow-up of the use of wells aspected in the study area, , the results showed 78% of the farmers had a medium degree of application of scientific suggestions related to the use of wells generally and tends to decrease. Also showed that 80% of farmers had medium level of application of recommendations in the field of well design and tended to be partially, and was clear that the percentage of farmers committed to the application of scientific recommendations related to the protection from pollution and follow-up of wells reached 76% at a medium level and tends to seriously apply of the instructions. the results showed a correlation for (level of education, time period of agricultural working, financial cost of drilling a well) with the general application, and it was also found that there is no correlation for the rest of the factors and the dependent variable.

Hybrid scientific article recommendation system with COOT optimization

Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold-start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and mean reciprocal rank (MRR) by 5.7%.

On-Farm Tree Mulberry Geometry: Biochemical, Growth and Yield Analysis in the Traditional Areas of Sericulture

Navigation from bush to tree mulberry plantation has been in force in recent years to conquer water scarcity and high-temperature impact in traditional areas. Despite no scientific recommendation, Seri-farmers are practicing varied geometry as per their knowledge and expediency wherein a systematic analysis of the growth, leaf quality, and yield parameters is lacking. Thus, tree mulberry gardens in traditional districts - Kolar and Chikkaballapura were selected to analyze the impact of varied geometry on biochemical, growth, and yield parameters of mulberry variety V1. Surprisingly, diverse geometry of 5'x5', 6'x6', 7'x5', 8'x4', 8'x8', 9'x5', 10'x5', 10'x10', and 12'x12' is in practice in the tree mulberry plantations of the study area. The foliage of tree mulberry raised under 12'x12' geometry possesses the highest total crude protein (30.81mg/100g), nitrogen (4.93%), phosphorus (0.39%), and potassium (1.66%) contents. Among growth yield parameters, 12'x12' geometry recorded the highest number of shoots per tree - 62, the number of leaves - 881, maximum shoot height - 157 cm, fresh weight of leaves - 4.18 g. However, leaf yield was recorded lowest (3.876 kg) in 12'x12' mulberry plantation over all the geometry where the number of trees accommodated per unit area was less. Further, the highest leaf moisture measuring 75.80% was recorded from 8'x4' mulberry plantation geometry. The data stated that the number of tree mulberry recorded was 3625 per hectare of plantation under 8'x4' geometry, giving rise to the highest leaf yield of 49209 kg per hectare per year with a gross return of Rs.761000. Computing all these data, we infer that rather than following capricious geometry for establishing tree mulberry plantation, we suggest following a scientific basis of geometry wherein mulberry crop quality and yield are steadfast to harvest cocoons qualitatively and quantitatively good.

Open educational resources (OER) and some of the United Nations sustainable development goals

Open educational resources (OER) and some of the United Nations sustainable development goals

309 Racial Discrimination in Surgical Practice: A Global Review

Abstract Introduction Racial discrimination indicates the unkind conduct towards other persons based on their race or skin color. We aimed to systematically review racial discrimination in surgery and answer the following questions:1) Does racial/ethnic discrimination in surgery exist in the last 5 years. 2) If yes, are ways suggested to reduce racial/ethnic discrimination in surgery? Method The systematic review was performed in compliance with the PRISMA guidelines along AMSTAR 2. A 5-year literature search was carried out on PubMed for articles published from 1/1/2017 to 01/11/2022. The retrieved citations were quality assessed by MERSQI and evidence graded by GRADE. Results A total of 9116 participants responded with a mean of 1013 (SD = 2408) responses per citations reported in 9 studies from a final list of 10 included citations. Nine studies were from USA and 1 from South Africa. The evidence of racial discrimination was justified on strong scientific evidence constituting the basis for grade I. The second question’s answer was ‘yes’ which was defendable on moderate scientific recommendation and thereby establishing the basis for evidence grade II. Conclusions There is sufficient evidence for the presence of racial discrimination in surgical practice in the last 5 years in the US. Ways to decrease racial discrimination in surgery exist. Healthcare and training systems must increase the awareness of these issues to eliminate the harmful effect on the individual and on the surgical team’s performance. The existence of the discussed problems needs to be managed in more countries with diverse healthcare systems.

SCIENTIFIC ARTICLES RECOMMENDATION SYSTEM BASED ON USER’S RELATEDNESS USING ITEM-BASED COLLABORATIVE FILTERING METHOD

Scientific article recommendation still remains one of the challenging issues in education, including learning process. Difficulties in finding related articles from research history and research interest have been experienced by students in collage affecting the duration of study and research time. This paper proposed a new solution by building a search engine to collect and to recommend articles related to student research topics. The system combined the web scraping method as an article data retrieval technique on google scholar and item-based collaborative filtering to recommend the article. Parameters result produced based on items of user’s history, including item-searched, clicked, and downloaded. The system was built on a web-based scientific article recommendation system using python programming language. This system recommends articles based on the preferences of users and other users who are affiliated and who have an interest in the same item. This research showed that the validation result from the system obtained a recommendation accuracy value over 0.516801. The percentage of the RMSE error value of the recommendation system is 8.62%, or in other words that the accuracy of the recommendation system is 91.28%.

O108 Racial discrimination in surgical practice: a 5 year global review

Abstract Introduction Racial discrimination indicates the unkind conduct towards other persons based on their race or skin colour. We aimed to systematically review racial discrimination in surgery and answer the following questions:1) Does racial/ethnic discrimination in surgery exist in the last 5 years. 2) If yes, are ways suggested to reduce racial/ethnic discrimination in surgery? Methods The systematic review was performed in compliance with the PRISMA guidelines along AMSTAR 2. A 5-year literature search was carried out on PubMed for articles published from 1/1/2017 to 01/11/2022. The retrieved citations were quality assessed by MERSQI and evidence graded by GRADE. Results A total of 9116 participants responded with a mean of 1013 (SD=2408) responses per citations reported in 9 studies from a final list of 10 included citations. Nine studies were from USA and 1 from South Africa. The evidence of racial discrimination was justified on strong scientific evidence constituting the basis for grade I. The second question's answer was ‘yes’ which was defendable on moderate scientific recommendation and thereby establishing the basis for evidence grade II. Conclusion There is sufficient evidence for the presence of racial discrimination in surgical practice in the last 5 years in the US. Ways to decrease racial discrimination in surgery exist. Healthcare and training systems must increase the awareness of these issues to eliminate the harmful effect on the individual and on the surgical team's performance. The existence of the discussed problems needs to be managed in more countries with diverse healthcare systems.

SCINOBO: a novel system classifying scholarly communication in a dynamically constructed hierarchical Field-of-Science taxonomy.

Classifying scientific publications according to Field-of-Science taxonomies is of crucial importance, powering a wealth of relevant applications including Search Engines, Tools for Scientific Literature, Recommendation Systems, and Science Monitoring. Furthermore, it allows funders, publishers, scholars, companies, and other stakeholders to organize scientific literature more effectively, calculate impact indicators along Science Impact pathways and identify emerging topics that can also facilitate Science, Technology, and Innovation policy-making. As a result, existing classification schemes for scientific publications underpin a large area of research evaluation with several classification schemes currently in use. However, many existing schemes are domain-specific, comprised of few levels of granularity, and require continuous manual work, making it hard to follow the rapidly evolving landscape of science as new research topics emerge. Based on our previous work of scinobo, which incorporates metadata and graph-based publication bibliometric information to assign Field-of-Science fields to scientific publications, we propose a novel hybrid approach by further employing Neural Topic Modeling and Community Detection techniques to dynamically construct a Field-of-Science taxonomy used as the backbone in automatic publication-level Field-of-Science classifiers. Our proposed Field-of-Science taxonomy is based on the OECD fields of research and development (FORD) classification, developed in the framework of the Frascati Manual containing knowledge domains in broad (first level(L1), one-digit) and narrower (second level(L2), two-digit) levels. We create a 3-level hierarchical taxonomy by manually linking Field-of-Science fields of the sciencemetrix Journal classification to the OECD/FORD level-2 fields. To facilitate a more fine-grained analysis, we extend the aforementioned Field-of-Science taxonomy to level-4 and level-5 fields by employing a pipeline of AI techniques. We evaluate the coherence and the coverage of the Field-of-Science fields for the two additional levels based on synthesis scientific publications in two case studies, in the knowledge domains of Energy and Artificial Intelligence. Our results showcase that the proposed automatically generated Field-of-Science taxonomy captures the dynamics of the two research areas encompassing the underlying structure and the emerging scientific developments.

Using an explicit query and a topic model for scientific article recommendation.

The search for relevant scientific articles is a crucial step in any research project. However, the vast number of articles published and available online in digital databases (Google Scholar, Semantic Scholar, etc.) can make this task tedious and negatively impact a researcher's productivity. This article proposes a new method of recommending scientific articles that takes advantage of content-based filtering. The challenge is to target relevant information that meets a researcher's needs, regardless of their research domain. Our recommendation method is based on semantic exploration using latent factors. Our goal is to achieve an optimal topic model that will serve as the basis for the recommendation process. Our experiences confirm our performance expectations, showing relevance and objectivity in the results.

A Scientific Paper Recommendation Framework Based on Multi-Topic Communities and Modified PageRank

Personalized PageRank is a variant of PageRank, widely developed for citation recommendation. However, the personalized PageRank that works with a vast amount and rich scholarly data still results in information overload. Sometimes, junior scholars still need help to arrange queries quickly because of limited domain knowledge. Senior researchers need reference papers regarding a similar topic they intend to search for and related topics as a new insight. In this research, scientific citation recommendation aims to find the most influential papers with similar and related topics. Related topic papers in serendipitous perspectives are reference papers that are novel, diversified and unexpected to a user. The unexpectedness of recommended papers can be papers with different topics to queries but still relevant. To accomplish these challenges, we propose a framework of scientific citation recommendation with serendipitous perspectives. The framework includes feature extraction of an academic citation network, selection of multi-topic communities, and ranking papers in the selected multi-topic communities by modified PageRank. Papers in the chosen communities tend to link to similar and related papers. Modified PageRank is an extension of personalized PageRank, which works on multi-topic communities and manuscript queries. The experiments reveal that the proposed models outperform some models of personalized PageRank and some models of Content-Based Filtering. The multi-topic communities-based models work more effectively than the baselines if they run in a large dataset since the topic communities become more cohesive.

WHO’s latest rabies recommendations and guidance save lives and reduce the cost of treatment

Rabies vaccination is a crucial part of rabies post-exposure prophylaxis (PEP), but it tends to consist of long and costly regimens of intramuscular (IM) injections. Most human rabies deaths are caused by delayed access, unaffordability or ineffective delivery of PEP. Reducing these barriers is crucial to ensure that this incurable yet preventable disease does not cost lives. In 2022, WHO published new guidance towards the introduction or expansion of rabies vaccination into national immunization programmes to systematically drive down human rabies deaths effectively and cost-efficiently. Such guidance grounds on the latest scientific recommendation provided by WHO’s Strategic Advisory Group of Experts in 2018. WHO recommends a shortened 1-week rabies vaccination schedule, with visits on days 0, 3 and 7. On each visit, a 2-site intradermal (ID) injection (using only 0.1 ml of vaccine in each site) is administered. ID administration allows for vials to be shared among several patients within a 6-8 hours timeline. Compared to IM administration, ID is cost- and dose-sparing, even in low-throughput clinics. Additionally, this regimen requires only 3 visits to the healthcare facility, improving patient compliance. However, the uptake of this shortened ID regimen remains limited. It should now be a matter of urgency for Health Ministries in rabies-endemic settings to adopt the WHO-recommended shortened ID vaccination schedule and ensure appropriate medical training to improve PEP delivery. This will enable countries to improve PEP delivery and allow underserved populations to access affordable, life-saving rabies vaccines.

HNERec: Scientific collaborator recommendation model based on heterogeneous network embedding

Scientific collaboration contributes to the innovation of science and technology. For individual scholars, it is valuable to find potential and valuable collaborators to improve the quality and quantity of research. Previous research mainly recommends collaborators by exploring one kind of relationship between authors, such as the research topics or co-authorship. Some research tries to utilize more relationships, but they simply integrate the recommendation results of different relationships rather than integrating them. This study proposes a novel Heterogeneous Network Embedding Recommendation (HNERec) model for scientific collaborator recommendation. The HNERec model employs a random walk to develop a heterogeneous network considering four kinds of meta-path illustrating the topic relationship, authorship, citation relationship, and venue relationship. It utilizes the skip-gram model to embed the nodes, and finally generates a recommendation list based on the similarity between the corresponding node vectors. Experiments on a DBLP dataset and an AMiner dataset demonstrate the reliability of the HNERec model. The HNERec model outperforms the mainstream collaborator recommendation baselines in metrics such as F1, MRR, and nDCG. The results can not only be applied to the selection of collaborators by individual scholars but also allow scientific and conference institutions to recommend collaborators.

Bone sarcoma follow-up; a nationwide analysis of oncological events after initial treatment

AimFollow-up strategies for high-grade bone sarcomas have been optimized to facilitate early detection of local recurrence and distant metastasis. The ideology is that early detection enables early treatment presuming better survival. However, the clinical value for each individual patient remains questionable. This study aims to evaluate oncological events after initial treatment in order to assess current follow-up strategies for high-grade bone sarcomas in the Netherlands. Patients and MethodsA retrospective cohort study was conducted based on a national registry. All cases were retrieved from the Netherlands Cancer Registry. Our study consisted of 393 patients treated between 2007 and 2011 with complete follow-up data. Baseline characteristics were analysed for all entities. Local recurrence and distant metastasis was analysed along with overall survival for high-grade chondrosarcoma, high-grade osteosarcoma, Ewing sarcoma and chordoma. ResultsMedian follow-up was 8,3 years for high-grade chondrosarcoma, 4,9 for high-grade osteosarcoma, 3,8 for Ewing sarcoma and 7,5 for chordoma. Median time to local recurrence and distant metastasis was 1,2 years for high-grade osteosarcoma and 1,5 years for Ewing sarcoma. For high-grade osteosarcoma with localized disease at presentation, 0.09 new distant metastatic events per patient per year were seen after five years of follow-up with 11,1 patients needed to follow-up for any event. Five-year overall survival was 60,0% for high-grade chondrosarcoma, 50,0% for high-grade osteosarcoma, 45,3% for Ewing sarcoma and 71,4% for chordoma. ConclusionsThis nationwide study shows a plateau in local recurrences and distant metastatic events after four years of treatment for patients with high-grade osteosarcoma and Ewing sarcoma. Due to a lack of reliable evidence however, we were not able to provide additional guidance on follow-up intervals and duration. Collaborative research with larger groups is needed in order to provide a solid scientific recommendation for follow-up in the heterogenous patient population with bone sarcoma.

Towards efficient navigation in digital libraries: Leveraging popularity, semantics and communities to recommend scholarly articles

• Our study proposes a novel algorithm, SPACE-R, which aims to improve the scalability and accuracy of large real-world academic recommendation systems. • This study incorporates the “quality” of papers by the construction of a Popularity Network, a derivative of the citation network, with importance given to author and journal impact. • The significance of a neighborhood network of a dynamic radius r has been demonstrated in the overall performance of the recommender system. • The judicious incorporation of a weighted sum of recency, quality, and semantic similarity as a personalization vector in the ranking process enhances the diversity of recommendations. • Community structure has been leveraged to get more topically similar papers as well as to reduce the search space, making the recommendation process scalable. The growing popularity of digital libraries as a medium for communicating scientific discoveries has made a large variety of research articles easily accessible. However, this constitutes a putative issue of information overloading with recommendation engines providing a compelling solution to the problem. Scientific Recommender Systems alleviate this problem by suggesting potential papers of interest to a user. For any researcher seeking developments in their field, it is important that the recommended papers are of high quality, recent and related to the field of interest, which has been largely overlooked in prior approaches. This study thus proposes a graph-based hybrid recommendation technique, SPACE-R , that amalgamates quality, semantic similarity and community structure of nodes in a citation network. The creation of a popularity network, a derivative of a citation network, in combination with a two-stage candidate selection process involving community detection and neighbourhood network identification, contributes to an improvement in the accuracy and scalability of the proposed model. The incorporation of semantic similarity achieves the necessary diversity in recommendations. Experimental evaluations on four large datasets against five baselines reveal that SPACE-R achieves an improvement of up to 45.53%, 56.76%, 49.39%, 46.84% and 78.18% in recall, precision, MRR, mAP, and response time, respectively.

Realist evaluation of three programmes aimed at reducing harm and risks associated with alcohol consumption in the Nouvelle Aquitaine region of France: the ECIAE study protocol

IntroductionIn Europe, alcohol consumption is responsible for many diseases, disabilities, injuries and premature deaths. In France, alcohol consumption represents an important health burden, due to its frequency, scale and the...

Application Analysis of Multi-Intelligence Optimization Decision-Making Method in College Students’ Ideological and Political Education System

Today’s society is a society with diversified information. The rapid change of information also affects the ideological and political education in universities. Therefore, the fixed thinking of ideological and political education in universities tells students, which is an educational way in line with the development of the times. We should follow up with the rapidly changing times at any time, change with the times, and update the thinking and systematic way of the ideological and political education system at any time. In the ideological and political education of college students, diversified teaching methods and multiobjective recommendation systems are implemented and then combined with traditional ideological and political teaching methods. The two ways complement each other and promote each other so as to achieve higher learning efficiency and better learning effect. The optimization algorithm of multiobjective recommendation should be used to further improve the ideological and political education system. By analyzing the optimization results and performance comparison of various algorithms, we find the most suitable algorithm model for optimizing the ideological and political education system. The multiobjective-recommended ideological and political education system for college students needs to fully improve the teaching tasks of teachers and students in two stages. A reasonable and scientific system recommendation mechanism should take into account students’ own learning preferences, subject types, ideological and political teacher information, curriculum information, curriculum evaluation, curriculum relevance, and other multiobjective data. This paper achieves the highest performance and the lowest time cost of the ideological and political education system through multiobjective evolutionary optimization method.