Final Recommendations Research Articles

Design and Implementation of an Intelligent Cross-Border E-Commerce English Learning Platform Based on a Knowledge Map

At present, most online education platforms still have problems such as single learning modes and loose knowledge structures. Using the knowledge map, the study employs a personalized adaptive intelligent adjustment strategy based on the structural expression of the CBEC English subject system. Firstly, the study uses the Scapy framework to crawl the subject knowledge data. Then use the LTP platform to process sentences containing multiple entities. Input the sentence into the dependency parser to analyze and extract the entity relationship. Finally, according to the relevance between knowledge points and topics in the knowledge map, the final learning path recommendation result is obtained. And cluster the similarity of curriculum content to build a complete curriculum system. Based on the above operations, a knowledge map-based smart learning platform for the CBEC English discipline has been designed and implemented to provide a smart, personalized learning environment for learners. According to the experimental analysis, the average satisfaction of learners with the learning platform designed by the study is 81.56%, which can meet the learning needs of learners and provide an excellent mobile learning environment for students.

Contesting colonial beachheads: Settler colonial (in)security professionals and Indigenous peoples’ energy infrastructure

In April 2020, the British Columbia Utilities Commission released the Final Report of its Indigenous Utilities Regulation Inquiry. The Inquiry was tasked with determining the regulatory environment for Indigenous utility providers across the Canadian province. We analyse the Inquiry as a colonial encounter between Indigenous nations and the settler state, arguing that technical bodies like the Commission represent a sort of security professional working to depoliticize the reproduction of settler sovereignty, highlighting the operations of non-traditional security professionals in settler colonial contexts. Through the Inquiry, security comes through providing certainty for existing energy infrastructure and institutions as ‘colonial beachheads’, legitimizing the settler colonial political-economic order. However, the Inquiry also demonstrates that such processes offer opportunities for Indigenous nations to contest that order. Indeed, Indigenous nations used the Inquiry to both contest the legitimization of settler sovereignty and accumulation, and press for the capacity to build their own infrastructure and institutions, thereby enacting their decision-making authority. We explore this tension in the context of the contemporary reconciliation discourse, finding the Inquiry’s final recommendations represent an attempt to reify settler sovereignty, while also deepening settler insecurity through the possibilities opened for greater Indigenous self-determination.

Recommendations for blood sampling in emergency departments from the European Society for Emergency Medicine (EUSEM), European Society for Emergency Nursing (EuSEN), and European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for the Preanalytical Phase. Executive summary.

Blood Sampling Guidelines have been developed totarget European emergency medicine-related professionals involved in the blood sampling process (e.g.physicians, nurses, phlebotomists working in the ED), as well as laboratory physicians and other related professionals. Theguidelines population focus on adult patients. The development of these blood sampling guidelines for the EDsetting isbased on the collaboration of three European scientific societies that have a role toplay in the preanalytical phaseprocess: EuSEN, EFLM, and EUSEM. The elaboration of the questions was doneusing the PICO procedure, literature search and appraisal was based on the GRADE methodology. The final recommendations were reviewed by an international multidisciplinary external review group. The document includes the elaborated recommendations for the selected sixteen questions. Three in pre-sampling, eight regarding sampling, three post-sampling, and two focus on quality assurance. In general, the quality ofthe evidence is very low, and the strength of the recommendation in all the questions has been rated as weak. The working group in four questions elaborate the recommendations, based mainly on group experience, rating as good practice. The multidisciplinary working group was considered one of the major contributors to this guideline. The lack of quality information highlights the need for research in this area of the patient care process. The peculiarities of the emergency medical areas need specific considerations to minimise the possibility of errors in the preanalytical phase.

An International, Multidisciplinary Consensus Set of Patient-Centered Outcome Measures for Substance-Related and Addictive Disorders.

Background: In 1990, the United States' Institute of Medicine promoted the principles of outcomes monitoring in the alcohol and other drugs treatment field to improve the evidence synthesis and quality of research. While various national outcome measures have been developed and employed, no global consensus on standard measurement has been agreed for addiction. It is thus timely to build an international consensus. Convened by the International Consortium for Health Outcomes Measurement (ICHOM), an international, multi-disciplinary working group reviewed the existing literature and reached consensus for a globally applicable minimum set of outcome measures for people who seek treatment for addiction. Methods: To this end, 26 addiction experts from 11 countries and 5 continents, including people with lived experience (n = 5; 19%), convened over 16 months (December 2018-March 2020) to develop recommendations for a minimum set of outcome measures. A structured, consensus-building, modified Delphi process was employed. Evidence-based proposals for the minimum set of measures were generated and discussed across eight videoconferences and in a subsequent structured online consultation. The resulting set was reviewed by 123 professionals and 34 people with lived experience internationally. Results: The final consensus-based recommendation includes alcohol, substance, and tobacco use disorders, as well as gambling and gaming disorders in people aged 12 years and older. Recommended outcome domains are frequency and quantity of addictive disorders, symptom burden, health-related quality of life, global functioning, psychosocial functioning, and overall physical and mental health and wellbeing. Standard case-mix (moderator) variables and measurement time points are also recommended. Conclusions: Use of consistent and meaningful outcome measurement facilitates carer-patient relations, shared decision-making, service improvement, benchmarking, and evidence synthesis for the evaluation of addiction treatment services and the dissemination of best practices. The consensus set of recommended outcomes is freely available for adoption in healthcare settings globally.

Value of Information for Clinical Trial Design: The Importance of Considering All Relevant Comparators.

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

As user–item interaction information is typically limited, collaborative filtering (CF)-based recommender systems often suffer from the data sparsity issue. To address this issue, recent recommender systems have turned to graph neural networks (GNNs) due to their superior performance in capturing high-order relationships. Furthermore, some of these GNN-based recommendation models also attempt to incorporate other information. They either extract self-supervised signals to mitigate the data sparsity problem or employ social information to assist with learning better representations under a social recommendation setting. However, only a few methods can take full advantage of these different aspects of information. Based on some testing, we believe most of these methods are complex and redundantly designed, which may lead to sub-optimal results. In this paper, we propose SSGCL, which is a recommendation system model that utilizes both social information and self-supervised information. We design a GNN-based propagation strategy that integrates social information with interest information in a simple yet effective way to learn user–item representations for recommendations. In addition, a specially designed contrastive learning module is employed to take advantage of the self-supervised signals for a better user–item representation distribution. The contrastive learning module is jointly optimized with the recommendation module to benefit the final recommendation result. Experiments on several benchmark data sets demonstrate the significant improvement in performance achieved by our model when compared with baseline models.

Surgery for advanced neuroendocrine tumours of the small bowel: recommendations based on a consensus meeting of the European Society of Endocrine Surgeons (ESES).

Small bowel neuroendocrine tumours often present with locally advanced or metastatic disease. The aim of this paper is to provide evidence-based recommendations regarding (controversial) topics in the surgical management of advanced small bowel neuroendocrine tumours. A working group of experts was formed by the European Society of Endocrine Surgeons. The group addressed 11 clinically relevant questions regarding surgery for advanced disease, including the benefit of primary tumour resection, the role of cytoreduction, the extent of lymph node clearance, and the management of an unknown primary tumour. A systematic literature search was performed in MEDLINE to identify papers addressing the research questions. Final recommendations were presented and voted upon by European Society of Endocrine Surgeons members at the European Society of Endocrine Surgeons Conference in Mainz in 2023. The literature review yielded 1223 papers, of which 84 were included. There were no randomized controlled trials to address any of the research questions and therefore conclusions were based on the available case series, cohort studies, and systematic reviews/meta-analyses of the available non-randomized studies. The proposed recommendations were scored by 38-51 members and rated 'strongly agree' or 'agree' by 64-96% of participants. This paper provides recommendations based on the best available evidence and expert opinion on the surgical management of locally advanced and metastatic small bowel neuroendocrine tumours.

Group travel recommendation model study based on spatial and temporal integration factors

With the increasing demand for tourism, people’s travel modes are more and more diversified, and the tourism recommendation system also arises at the historical juncture. However, the current recommendation system is only recommended for a single user and does not realize the group travel recommendation. To achieve the goal of recommending its preferred attractions for multiple users, the time decay characteristics and Pearson correlation coefficient in Newton’s cooling law are used to obtain the user similarity with spatial distance factor and temporal decay factor and to obtain the score prediction results based on spatiotemporal fusion. In addition, the trust of user communication is used to recommend, and the weights of the two scoring results are added to obtain the personalized recommendation results of member users. Finally, the study used the fusion strategy to integrate the personalized recommendation results for group preference and obtained the final group travel recommendation list. Therefore, a group travel recommendation model based on spatio-temporal integration factors was constructed. According to the experimental analysis, we can see that the average HR value of the constructed model is 0.8124, and the average NDCG value is 0.7284, which can accurately judge users’ preferences and get the most suitable group travel recommendation results, thus facilitating users to make the next plan for the tourism project.

Pan American League of Associations for Rheumatology Recommendations for the Treatment of Psoriatic Arthritis.

Psoriatic arthritis (PsA) is chronic disease that compromises multiple domains and might be associated with progressive joint damage, increased mortality, functional limitation, and considerably impaired quality of life. Our objective was to generate evidence-based recommendations on the management of PsA in Pan American League of Associations for Rheumatology (PANLAR) countries. We used the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE)-ADOLOPMENT approach to adapt the 2019 recommendations of the European Alliance of Associations for Rheumatology. A working group consisting of rheumatologists from various countries in Latin America identified relevant topics for the treatment of PsA in the region. The methodology team updated the evidence and synthesized the information used to generate the final recommendations. These were then discussed and defined by a panel of 31 rheumatologists from 15 countries. Theses guidelines report 15 recommendations addressing therapeutic targets, use of antiinflammatory agents and corticosteroids, treatment with disease-modifying antirheumatic drugs (conventional synthetic, biologic, and targeted synthetic), therapeutic failure, optimization of biologic therapy, nonpharmacological interventions, assessment tools, and follow-up of patients with PsA. Here we present a set of recommendations to guide decision making in the treatment of PsA in Latin America, based on the best evidence available, considering resources, medical expertise, and the patient's values and preferences. The successful implementation of these recommendations should be based on clinical practice conditions, healthcare settings in each country, and a tailored evaluation of patients.

Contact measurement of skin temperature using a wearable two-channel PPG optical sensor supplemented by thermometers

Abstract This paper describes realization, basic properties, testing, and experiments with a special prototype of a wearable two-channel photoplethysmography (PPG) sensor supplemented by contact thermometers that maps the skin temperature at the place where the optical part of the sensor touches a measured hand part (typically a wrist and fingers). Preliminary measurement confirms that proposed I2C thermometers have proper stability and precision, so can be successfully used in the developed PPG sensor. The performed main experiments show that the significant increase of temperature was always observed at the place of the worn PPG sensors during the whole measurement. This rise of temperature affects mainly the PPG signal range, thus it has also influence on the quality of the sensed PPG wave. The conclusion formulates the final recommendation about the necessity of at least 5-minute time delay between the PPG sensor placement on the hand and the start of the PPG signal acquisition. While the current measurements were realized in normal laboratory conditions, the whole wearable PPG sensor consists of non-ferromagnetic materials and all parts are fully shielded by aluminum boxes to enable measurement in a low magnetic field environment which, is our final long term research aim.

한국과 일본의 통합교육 관련법 비교 연구

[Purpose] This study aimed to compare and analyze the laws related to inclusive education in South Korea and Japan according to Article 24 of the UN CRPD, in order to draw implications necessary for realizing inclusive education that meets the standards of the international community.
 [Methods] To achieve this, an analysis framework was developed based on the text of Article 24 of the Convention, and the inclusive education laws of both countries were compared.
 [Results] The main research findings are as follows. First, both countries have established legal foundations for the philosophy of inclusive education, but Japan has systematically laid the legal groundwork for the construction of an inclusive education system by revising its laws in accordance with the UN CRPD. Second, the responsibilities of the national and local governments for promoting inclusive education are specified in the laws of both countries, but whereas Korea focuses on special education policies, Japan manages policies comprehensively among various ministries. Third, in terms of support at the level of institutions operating inclusive education, Korea specifies the relevant content in law more concretely than Japan, but it was found that there is a lack of manuals or guidelines that can be utilized in the field.
 [Conclusion] Based on these findings, proposals were made for the legal and environmental improvements necessary to enhance the quality of inclusive education in Korea, in line with the final recommendations issued by the UN CRPD.

Finite Element Analysis for Degenerative Cervical Myelopathy: Scoping Review of the Current Findings and Design Approaches, Including Recommendations on the Choice of Material Properties.

Degenerative cervical myelopathy (DCM) is a slow-motion spinal cord injury caused via chronic mechanical loading by spinal degenerative changes. A range of different degenerative changes can occur. Finite element analysis (FEA) can predict the distribution of mechanical stress and strain on the spinal cord to help understand the implications of any mechanical loading. One of the critical assumptions for FEA is the behavior of each anatomical element under loading (ie, its material properties). This scoping review aims to undertake a structured process to select the most appropriate material properties for use in DCM FEA. In doing so, it also provides an overview of existing modeling approaches in spinal cord disease and clinical insights into DCM. We conducted a scoping review using qualitative synthesis. Observational studies that discussed the use of FEA models involving the spinal cord in either health or disease (including DCM) were eligible for inclusion in the review. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The MEDLINE and Embase databases were searched to September 1, 2021. This was supplemented with citation searching to retrieve the literature used to define material properties. Duplicate title and abstract screening and data extraction were performed. The quality of evidence was appraised using the quality assessment tool we developed, adapted from the Newcastle-Ottawa Scale, and shortlisted with respect to DCM material properties, with a final recommendation provided. A qualitative synthesis of the literature is presented according to the Synthesis Without Meta-Analysis reporting guidelines. A total of 60 papers were included: 41 (68%) "FEA articles" and 19 (32%) "source articles." Most FEA articles (33/41, 80%) modeled the gray matter and white matter separately, with models typically based on tabulated data or, less frequently, a hyperelastic Ogden variant or linear elastic function. Of the 19 source articles, 14 (74%) were identified as describing the material properties of the spinal cord, of which 3 (21%) were considered most relevant to DCM. Of the 41 FEA articles, 15 (37%) focused on DCM, of which 9 (60%) focused on ossification of the posterior longitudinal ligament. Our aggregated results of DCM FEA indicate that spinal cord loading is influenced by the pattern of degenerative changes, with decompression alone (eg, laminectomy) sufficient to address this as opposed to decompression combined with other procedures (eg, laminectomy and fusion). FEA is a promising technique for exploring the pathobiology of DCM and informing clinical care. This review describes a structured approach to help future investigators deploy FEA for DCM. However, there are limitations to these recommendations and wider uncertainties. It is likely that these will need to be overcome to support the clinical translation of FEA to DCM.

Review-Enhanced Hierarchical Contrastive Learning for Recommendation

Designed to establish potential relations and distill high-order representations, graph-based recommendation systems continue to reveal promising results by jointly modeling ratings and reviews. However, existing studies capture simple review relations, failing to (1) completely explore hidden connections between users (or items), (2) filter out redundant information derived from reviews, and (3) model the behavioral association between rating and review interactions. To address these challenges, we propose a review-enhanced hierarchical contrastive learning, namely ReHCL. First, ReHCL constructs topic and semantic graphs to fully mine review relations from different views. Moreover, a cross-view graph contrastive learning is used to achieve enhancement of node representations and extract useful review knowledge. Meanwhile, we design a neighbor-based positive sampling to capture the graph-structured similarity between topic and semantic views, further performing efficient contrast and reducing redundant noise. Next, we propose a cross-modal contrastive learning to match the rating and review representations, by exploring the association between ratings and reviews. Lastly, these two contrastive learning modes form a hierarchical contrastive learning task, which is applied to enhance the final recommendation task. Extensive experiments verify the superiority of ReHCL compared with state-of-the-arts.

A Comparative Analysis of Object Identification Labelling Platforms: Basketball Perspective

Manual object identification labelling is laborious, time-consuming and prone to inconsistencies hindering advancements in various computer vision tasks.These inconsistencies can lead to inaccurate models with poor performance. Considering these potential consequences, highlights the importance of addressing labelling challenges for ethical and responsible AI development. To address this our study evaluates several popular platforms for their suitability in tackling these challenges. Roboflow, Makesense.ai, SentiSight.ai, Labelbox and SuperAnnotate are the five different data labelling platforms that have been taken for assessment. The study identifies strengths and weaknesses of each platform in the context of basketball detection using YOLO v8, a deep learning model for object detection, image classification, and image segmentation. Each platform is analysed based on features, ease of use, pricing, and support for image annotation, object detection, and YOLO v8 integration. After analysing these factors, a final recommendation is made, highlighting the platform that demonstrably offers the best balance of features, efficiency, and cost-effectiveness for this specific task. The study helps in deeper exploration of the potential of YOLO v8. It is mainly aimed at assisting the Video Assistant Referees(VARs) for accurate and unbiased decision-making and also empowers the development of AI technology across the domain of sports.

Truth Commissions and the Prevention of Targeted Mass Killings

ABSTRACT While transitional justice endeavors aim to help countries come to terms with violent pasts, policymakers and practitioners often claim that transitional justice mechanisms help prevent future violence as well. No cross-national research has tested this claim, however. This article begins to fill this gap by examining whether one of the most frequently used mechanisms of transitional justice, the truth commission, is associated with the onset of targeted mass killings (TMKs) in 27 countries between 1972 and 2018. Our differences-in-differences estimation approach finds that countries that have implemented truth commissions see a significant reduction in the recurrence of TMKs compared to those countries that did not implement a truth commission. Additional analyses reveal that while issuance of a final report and recommendations for reforms are not associated with the onset of TMKs, truth commissions that do not recommend the punishment of perpetrators are more likely to reduce TMKs in the years following the truth commission. The article ends by discussing the functions of truth commissions and proposing how their preventive capacity may be strengthened further through the application of an atrocity prevention lens.

A long-tail alleviation post-processing framework based on personalized diversity of session recommendation

Session-based recommendation leverages the short-term interaction sequence to predict the next item a user is most likely to click on. Generally, in real applications, users often click on different types of items in the same session, which makes the items in the sequence present diversity, and degree of diversity vary with different sequences, that is, there is a phenomenon of personalized diversity. However, the majority of existing session recommendation models primarily focus on improving recommendation accuracy and overlooking the importance of personalized diversity, which leads to the issue of filtering bubbles. In order to alleviate the long-tail effect caused by the filtering bubble, the recommendation system should not only consider the accuracy of recommended items, but also the diversity of users’ demands for recommended items. To address this issue, this paper proposes a model, named as LAP-SR, a Long-tail Alleviation Post-processing framework based on personalized diversity of Session Recommendation. After obtaining the initial recommendation list through the selected initial session model, LAP-SR model generates the long-tail item set and item graph according to session contents. Besides, from the item graph, the model calculates the embedding representation of items, from which the diversity of all sessions is calculated. By combining the obtained diversity and the long-tail item set, the initial recommendation list is soft processed. On this basis, the model yields the final recommendation list, which alleviates the overall long-tail effect. We verified that as a post-processing framework, the proposed model can be applied to various session recommendation models. Extensive experiments on the three datasets of Diginetica, Tmall and Yoochoose1_64 demonstrate that the LAP-SR model has a competitive advantage over the baseline models in mitigating the long-tail effect.

The conflict between Rights of Nature and mining in Ecuador: Implications of the Los Cedros Cloud Forest case for biodiversity conservation

Abstract Global emergence of Rights of Nature (RoN) has gained momentum since Ecuador became the first country to constitutionally recognize it in 2008. The shift from perceiving nature as an object, to granting it legal subjecthood, can revolutionize protection of ecological systems. In 2021, Ecuador's Constitutional Court issued a landmark ruling, halting mining in the Los Cedros Protected Forest. Three pillars form the basis for legal protection of Los Cedros: (i) the right to timely, Free Prior Informed Environmental Consultation, (ii) application of the Precautionary Principle in risk to RoN, and (iii) the Right to Water. We analyse the Court ruling to identify legal frameworks applied then map and rank mining risk to other protected forests, Indigenous territories, unprotected native ecosystems, biodiversity and areas of water resource conflict to determine potential scale of conflict between mining and RoN. 7813 mining concessions of 22,812km2 overlay 9.2% of Ecuadorian mainland, 2323 concessions (29.7%) overlap 16,081km2 of protected forest (4781 km2, 20%), Indigenous territory (6473 km2, 8%) and native vegetation outside protected areas and Indigenous territories (13,390 km2, 9%). With 80% of their protected forests at risk from large‐scale mining, the most impacted Indigenous communities are the Shuar. Synthesis and applications: The Los Cedros legal case in Ecuador sets a precedent for using RoN to challenge mining in 4781 km2 of similar Protected Forest, with potential to protect an additional 16,081 km2 of Indigenous lands and biologically important ecosystems. However, lack of biological data for these areas will necessitate extensive data collection, possibly through community‐empowering citizen science. Our study emphasizes the urgent need to integrate indigenous and traditional ecological knowledge (ITEK), law and ecology. We propose a new transdisciplinary field of ‘ecological forensics’ to support nature protection within the RoN framework. Our research also identifies areas where RoN could effectively protect nature and that are likely to be of high investment risk for the mining industry. The final recommendation is to reconsider mining concessions in Ecuador, especially in ecologically sensitive areas, Indigenous territories, high biodiversity areas, and regions with water resource conflicts, to maintain ecological integrity and social harmony. Read the free Plain Language Summary for this article on the Journal blog.

Personalized Tourist Recommender System: A Data-Driven and Machine-Learning Approach

This study introduces a data-driven and machine-learning approach to design a personalized tourist recommendation system for Nepal. It examines key tourist attributes, such as demographics, behaviors, preferences, and satisfaction, to develop four sub-models for data collection and machine learning. A structured survey is conducted with 2400 international and domestic tourists, featuring 28 major questions and 125 variables. The data are preprocessed, and significant features are extracted to enhance the accuracy and efficiency of the machine-learning models. These models are evaluated using metrics such as accuracy, precision, recall, F-score, ROC, and lift curves. A comprehensive database for Pokhara City, Nepal, is developed from various sources that includes attributes such as location, cost, popularity, rating, ranking, and trend. The machine-learning models provide intermediate categorical recommendations, which are further mapped using a personalized recommender algorithm. This algorithm makes decisions based on weights assigned to each decision attribute to make the final recommendations. The system’s performance is compared with other popular recommender systems implemented by TripAdvisor, Google Maps, the Nepal tourism website, and others. It is found that the proposed system surpasses existing ones, offering more accurate and optimized recommendations to visitors in Pokhara. This study is a pioneering one and holds significant implications for the tourism industry and the governing sector of Nepal in enhancing the overall tourism business.

Meta-path aware dynamic graph learning for friend recommendation with user mobility

Recently, friend recommendation has gained widespread popularity in location-based social networks (LBSNs), which provides more opportunities for users to forge new friendships. Most existing studies exploit user trajectories or check-ins of Point-Of-Interests (POIs) to predict friendships based on geographic homophily. However, the dynamics of social relationships are left insufficiently considered in modeling user preferences. In this paper, we explore how geographical and social preferences influence each other in a dynamic manner. Specifically, we propose a Meta-path aware Dynamic Graph with Subgraph Inference, named MDyGSI, which models the evolution of user preferences with time-phased sequences of POIs and social relationships for friend recommendation in LBSNs. In each time step of the evolution, geographical and social preferences are modeled through behavior-specific meta-paths in a dynamic heterogeneous graph. The formations of different meta-paths are facilitate by each other to explore mutual influences of dual preferences. To keep the dynamics of social relationships aligned with check-in history, reliable User-User connections are sampled from social graphs based on geographical collaborative filtering in each step, which also avoids noisy social interactions. Furthermore, the dual preferences are concatenated with evolutionary weights measured by a Gated Recurrent Unit for final recommendation. Experimental results on two real-world datasets show significant improvements in MDyGSI over the state-of-the-art methods.

AO Spine/Praxis Clinical Practice Guidelines for the Management of Acute Spinal Cord Injury: An Introduction to a Focus Issue.

Narrative overview and summary. The objective of this introductory manuscript is to provide an overview of the effort that was undertaken to establish clinical practice guidelines for a number of important topics in spinal cord injury (SCI). These topics included: 1. The role and timing of surgical decompression after acute traumatic SCI; 2. The hemodynamic management of acute traumatic SCI; and 3. The definition, diagnosis, and management of intra-operative SCI. Here, we introduce the rationale for the guidelines, the methodology utilized, and summarize how the topics are addressed within various manuscripts of this Focus Issue. The key clinical questions were defined using the PICO format for treatment reviews (patient; intervention; comparison; outcomes) or PPO format (patient, prognostic factor, outcomes) for risk factor review. Multi-disciplinary, international guideline development groups (GDGs) were established to evaluate and collate the available evidence in a rigorous, systematic manner, followed by a review of systematically obtained evidence within the framework of the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) criteria and application of the Evidence to Decision process. Consensus meetings, using a modified Delphi approach, were held with the multidisciplinary, international GDGs using online video-conferencing technology and anonymous voting to develop the final recommendations for each of the topics addressed. All systematic review protocols followed PRISMA standards and were registered on PROSPERO; all potential conflicts were vetted in an open and transparent manner. The funders (AO Spine and Praxis Spinal Cord Institute) had no influence over editorial content or the guidelines process). Updated guidelines were established for the timing of surgical decompression after acute SCI, with surgical decompression within 24hours of injury now "recommended" as a treatment option. Updated guidelines were also established for hemodynamic management, with an expanded target range for mean arterial pressure (MAP) of 75-80 to 90-95mmHg for between 3 to 7days post-injury now "suggested" as a treatment option. The available literature mandated scoping and systematic reviews on the topic of intra-operative SCI, and this resulted in manuscripts to address the definition, frequency, and risk factors, to define the role of intra-operative neuromonitoring, and to suggest an evidence-based care pathway for management. A rigorous process following GRADE standards was undertaken to review the available evidence and establish guideline recommendations around the role and timing of surgery in acute SCI, optimal hemodynamic management of acute SCI and the prevention, diagnosis and management of intraoperative SCI. This effort also identified key knowledge gaps and future directions for study, which will serve to refine these recommendations in the future.