Multiple Entities Research Articles

Legal Norms for the Protection of labor rights of Multinational Enterprises: Based on the Perspective of Comparative Law

With the development of economic globalization, the scale of multinational enterprises continues to expand, the number of labors working in multinational enterprises is rapidly increasing while labor rights of multinational enterprises are frequently violated. How to protect human rights of those labors has become a challenge that current world must face. The home countries, host countries, international organization and multinational enterprises fail to undertake their labor rights protection responsibilities in many times, and there are significant deficiencies in legislation here. Nowadays, multiple entities are in the practice of overseas labor protection in order to promote the world's sustainable development, integrate the interests and achieve the cause of global human rights protection career. This essay will use historical research, comparative analysis, regulatory research to provide ideas and solutions for the better realization and legislation of labor rights protection from the aspects of the history of particular labor rights protection, the concepts and characteristics of relevant protection subjects, the main reasons for the current situation of infringement, theoretical basis of labor rights protection, legislative impetus, final expectations for international actors in action.

Medical Named Entity Recognition Fusing Part-of-Speech and Stroke Features

It is highly significant from a research standpoint and a valuable practice to identify diseases, symptoms, drugs, examinations, and other medical entities in medical text data to support knowledge maps, question and answer systems, and other downstream tasks that can provide the public with knowledgeable answers. However, when contrasted with other languages like English, Chinese words lack a distinct dividing line, and medical entities have problems such as long length and multiple entity types nesting. Therefore, to address these issues, this study suggests a medical named entity recognition (NER) approach that combines part-of-speech and stroke features. First, the text is fed into the BERT pre-training model to get the semantic representation of the text, while the part-of-speech feature vector is obtained using the part-of-speech dictionary, and the stroke feature of the text is extracted through a convolution neural network (CNN). The word vector is then joined with the part-of-speech and stroke feature vectors, respectively, and input into the BiLSTM and CRF layer for training. Additionally, to balance the disparity in data volume across several types of entities, the class-weighted loss function is included in the loss function. According to the experimental findings, our model’s F1 score on the CCKS2019 dataset reaches 78.65%, and the recognition performance exceeds many existing algorithms.

Interested but Uncertain: Carbon Markets and Data Sharing among U.S. Crop Farmers

The potential for farmers and agriculture to sequester carbon and contribute to global climate change goals is widely discussed. However, there is currently low participation in agricultural carbon markets and a limited understanding of farmer perceptions and willingness to participate. Furthermore, farmers’ concerns regarding data privacy may complicate participation in agricultural carbon markets, which necessitates farmer data sharing with multiple entities. This study aims to address research gaps by assessing farmers’ willingness to participate in agricultural carbon markets, identifying the determinants of farmers’ willingness regarding carbon markets participation, and exploring how farmers’ concerns for data privacy relate to potential participation in agricultural carbon markets. Data were collected through a multistate survey of 246 farmers and analyzed using descriptive statistics, factor analysis, and multinomial regression models. We find that the majority of farmers (71.8%) are aware of carbon markets and would like to sell carbon credits, but they express high uncertainty about carbon market information, policies, markets, and cost impacts. Just over half of farmers indicated they would share their data for education, developing tools and models, and improving markets and supply chains. Farmers who wanted to participate in carbon markets were more likely to have higher farm revenues, more likely to share their data overall, more likely to share their data with private organizations, and more likely to change farming practices and had more positive perceptions of the impact of carbon markets on farm profitability. In conclusion, farmers have a general interest in carbon market participation, but more information is needed to address their uncertainties and concerns.

Developing a four-entities reinforced rank model to evaluate the topic influence in academic networks

Several studies have reported on metrics for measuring the influence of scientific topics from different perspectives; however, current ranking methods ignore the reinforcing effect of other academic entities on topic influence. In this paper, we developed an effective topic ranking model, 4EFRRank, by modeling the influence transfer mechanism among all academic entities in a complex academic network using a four-layer network design that incorporates the strengthening effect of multiple entities on topic influence. The PageRank algorithm is utilized to calculate the initial influence of topics, papers, authors, and journals in a homogeneous network, whereas the HITS algorithm is utilized to express the mutual reinforcement between topics, papers, authors, and journals in a heterogeneous network, iteratively calculating the final topic influence value. Based on a specific interdisciplinary domain, social media data, we applied the 4ERRank model to the 19,527 topics included in the criteria. The experimental results demonstrate that the 4ERRank model can successfully synthesize the performance of classic co-word metrics and effectively reflect high citation topics. This study enriches the methodology for assessing topic impact and contributes to the development of future topic-based retrieval and prediction tasks.

Nationwide volume-outcome relationship concerning in-hospital mortality and failure-to-rescue in surgery of sigmoid diverticulitis.

A correlation between the hospital volume and outcome is described for multiple entities of oncological surgery. To date, this has not been analyzed for the surgical treatment of sigmoid diverticulitis. The aim of this study was to explore the impact of the annual caseload per hospital of colon resection on the postoperative incidence of complications, failure to rescue, and mortality in patients with diverticulitis. Patients receiving colorectal resection independent from the diagnosis from 2012 to 2017 were selected from a German nationwide administrative dataset. The hospitals were grouped into five equal caseload quintiles (Q1-Q5 in ascending caseload order). The outcome analysis was focused on patients receiving surgery for sigmoid diverticulitis. In total, 662,706 left-sided colon resections were recorded between 2012 and 2017. Of these, 156,462 resections were performed due to sigmoid diverticulitis and were included in the analysis. The overall in-house mortality rate was 3.5%, ranging from 3.8% in Q1 (mean of 9.5 procedures per year) to 3.1% in Q5 (mean 62.8 procedures per year; p < 0.001). Q5 hospitals revealed a risk-adjusted odds ratio of 0.85 (95% CI 0.78-0.94; p < 0.001) for in-hospital mortality compared to Q1 during multivariable logistic regression analysis. High-volume centers showed overall lower complication rates, whereas the failure-to-rescue did not differ significantly. Surgical treatment of sigmoid diverticulitis in high-volume colorectal centers shows lower postoperative mortality rates and fewer postoperative complications.

The tutor team construction in the "honeycomb model" of professional degrees training for postgraduate

This paper analyzes the current situation of professional degree graduate education in China, summarizes the main problems existing in the construction of tutor team at the present stage, and constructs the "honeycomb model" of civil engineering master's degree graduate training. The "honeycomb model" replaces the double mentors with the mentor team, and takes the cultivation of practical innovation ability as the goal. Demand-oriented, it establishes substantive connections between the multiple agents represented by the mentor team members. In addition, the "honeycomb model" promotes the mutual promotion and integration of multiple entities through cost-effectiveness, and realizes the sustainable development of multiple entities through win-win cooperation. Finally, the model relies on the project matrix operation mode to promote the construction of tutor team and realize the unification of academic and professional graduate training.

Investigating students’ development of mechanistic reasoning in modeling complex aquatic ecosystems

IntroductionThis study reports on a classroom intervention where upper-elementary students and their teacher explored the biological phenomena of eutrophication using the Modeling and Evidence Mapping (MEME) software environment and associated learning activities. The MEME software and activities were designed to help students create and refine visual models of an ecosystem based on evidence about the eutrophication phenomena. The current study examines how students utilizing this tool were supported in developing their mechanistic reasoning when modeling complex systems. We ask the following research question: How do designed activities within a model-based software tool support the integrations of complex systems thinking and the practice of scientific modeling for elementary students?MethodsThis was a design-based research (DBR) observational study of one classroom. A new mechanistic reasoning coding scheme is used to show how students represented their ideas about mechanisms within their collaboratively developed models. Interaction analysis was then used to examine how students developed their models of mechanism in interaction.ResultsOur results revealed that students’ mechanistic reasoning clearly developed across the modeling unit they participated in. Qualitative coding of students’ models across time showed that students’ mechanisms developed from initially simplistic descriptions of cause and effect aspects of a system to intricate connections of how multiple entities within a system chain together in specific processes to effect the entire system. Our interaction analysis revealed that when creating mechanisms within scientific models students’ mechanistic reasoning was mediated by their interpretation/grasp of evidence, their collaborative negotiations on how to link evidence to justify their models, and students’ playful and creative modeling practices that emerged in interaction.DiscussionIn this study, we closely examined students’ mechanistic reasoning that emerge in their scientific modeling practices, we offer insights into how these two theoretical frameworks can be effectively integrated in the design of learning activities and software tools to better support young students’ scientific inquiry. Our analysis demonstrates a range of ways that students represent their ideas about mechanism when creating a scientific model, as well as how these unfold in interaction. The rich interactional context in this study revealed students’ mechanistic reasoning around modeling and complex systems that may have otherwise gone unnoticed, suggesting a need to further attend to interaction as a unit of analysis when researching the integration of multiple conceptual frameworks in science education.

Vacuoles, E1 enzyme, X-linked, autoinflammatory, and somatic syndrome in the intensive care unit: a case report

BackgroundVacuoles, E1 enzyme, X-linked, autoinflammatory, and somatic syndrome is a newly discovered inflammatory disease affecting male subjects, for which few data exist in the literature. Here, we describe the case of a patient with known Sweet’s syndrome admitted to the intensive care unit and for whom a vacuoles, E1 enzyme, X-linked, autoinflammatory, and somatic syndrome was diagnosed, allowing for appropriate treatment and the patient’s discharge and recovery.Case presentationA 70-year-old male White patient was hospitalized in the intensive care unit following an intrahospital cardiac arrest. History started a year before with repeated deep vein thrombosis and episodes of skin eruption compatible with Sweet’s syndrome. After a course of oral steroids, fever and inflammatory syndrome relapsed with onset of polychondritis, episcleritis along with neurological symptoms and pulmonary infiltrates. Intrahospital hypoxic cardiac arrest happened during patient’s new investigations, and he was admitted in a critical state. During the intensive care unit stay, he presented with livedoid skin lesions on both feet. Vasculitis was not proven; however, cryoglobulinemia screening came back positive. Onset of pancytopenia was explored with a myelogram aspirate. It showed signs of dysmyelopoiesis and vacuoles in erythroid and myeloid precursors. Of note, new deep vein thrombosis developed, despite being treated with heparin leading to the diagnosis of heparin-induced thrombocytopenia. The course of symptoms were overlapping multiple entities, and so a multidisciplinary team discussion was implemented. Screening for UBA1-mutation in the blood came back positive, confirming the vacuoles, E1 enzyme, X-linked, autoinflammatory, and somatic syndrome. Corticosteroids and anti-IL1 infusion were started with satisfactory results supporting patient’s discharge from intensive care unit to the internal medicine ward.ConclusionsVacuoles, E1 enzyme, X-linked, autoinflammatory, and somatic syndrome should be suspected in male patients presenting with inflammatory symptoms, such as fever, skin eruption, chondritis, venous thromboembolism, and vacuoles in bone marrow precursors. Patients with undiagnosed vacuoles, E1 enzyme, X-linked, autoinflammatory, and somatic syndrome may present with organ failure requiring hospitalization in intensive care unit, where screening for UBA1 mutation should be performed when medical history is evocative. Multidisciplinary team involvement is highly recommended for patient management, notably to start appropriate immunosuppressive treatments.

The European experience with testing and surveillance during the first phase of the COVID-19 pandemic

BackgroundCOVID-19 pandemic provides a unique opportunity to learn the challenges encountered by public health emergency preparedness systems, both in terms of problems encountered and adaptations during and after the first wave, as well as successful responses to them.ResultsThis work draws on published literature, interviews with countries and institutional documents as part of a European Centre for Disease Prevention and Control project that aims to identify the implications for preparedness measurement derived from COVID-19 pandemic experience in order to advance future preparedness efforts in European Union member states. The analysis focused on testing and surveillance themes and five countries were considered, namely Italy, Germany, Finland, Spain and Croatia. Our analysis shown that a country’s ability to conduct testing at scale was critical, especially early in the pandemic, and the inability to scale up testing operations created critical issues for public health operations such as contact tracing. Countries were required to develop new strategies, approaches, and policies under pressure and to review and revise them as the pandemic evolved, also considering that public health systems operate at the national, regional, and local level with respect to testing, contact tracing, and surveillance, and involve both government agencies as well as private organizations. Therefore, communication among multiple public and private entities at all levels and coordination of the testing and surveillance activities was critical.ConclusionWith regard to testing and surveillance, three capabilities that were essential to the COVID-19 response in the first phase, and presumably in other public health emergencies: the ability to scale-up testing, contact tracing, surveillance efforts; flexibility to develop new strategies, approaches, and policies under pressure and to review and revise them as the pandemic evolved; and the ability to coordinate and communicate in complex public health systems that operate at the national, regional, and local level with respect and involve multiple government agencies as well as private organizations.

ECHO: A hierarchical combination of classical and multi-agent epistemic planning problems

Abstract The continuous interest in Artificial Intelligence (AI) has brought, among other things, the development of several scenarios where multiple artificial entities interact with each other. As for all the other autonomous settings, these multi-agent systems require orchestration. This is, generally, achieved through techniques derived from the vast field of Automated Planning. Notably, arbitration in multi-agent domains is not only tasked with regulating how the agents act, but must also consider the interactions between the agents’ information flows and must, therefore, reason on an epistemic level. This brings a substantial overhead that often diminishes the reasoning process’s usability in real-world situations. To address this problem, we present ECHO, a hierarchical framework that embeds classical and multi-agent epistemic (epistemic, for brevity) planners in a single architecture. The idea is to combine (i) classical; and(ii) epistemic solvers to model efficiently the agents’ interactions with the (i) ‘physical world’; and(ii) information flows, respectively. In particular, the presented architecture starts by planning on the ‘epistemic level’, with a high level of abstraction, focusing only on the information flows. Then it refines the planning process, due to the classical planner, to fully characterize the interactions with the ‘physical’ world. To further optimize the solving process, we introduced the concept of macros in epistemic planning and enriched the ‘classical’ part of the domain with goal-networks. Finally, we evaluated our approach in an actual robotic environment showing that our architecture indeed reduces the overall computational time.

Does the world need to change its vaccine distribution strategy for COVID-19?

During an emergency, there are many activities in a pharmaceutical supply chain (PSC) which are rendered ineffective. This study aims to propose a holistic approach for Big Data Driven Pharmaceuticals Supply Chain (BDDPSC) during medical emergencies. During unprecedented situations, the quality of healthcare services by using traditional supply chain becomes a challenge. The current study aims to generate a model wherein multiple entities will be the part of data entries like hospitals, clinical trials, medical practitioners, and drug manufacturing companies during COVID-19. The study has considered certified medical practitioners as the experts and based their responses for proposing a theoretical framework deploying E-Delphi–Qualitative Data Analysis approach. By critically examining experts’ responses and comments, the study formulated four major themes and ten sub-themes for smooth functioning of BDDPSCs during an emergency. The E-Delphi was conducted in two rounds to reach final consensus and to find a balance for PSC in terms of efficiency and quality. This research is novel wherein big data enabled PSC theoretical model has been formulated using a qualitative approach for handling COVID-19. The proposed framework provides an enriched way to capture data from the important link viz. “health officials” of PSCs.

Melding wildlife surveys to improve conservation inference.

Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several datasets. Individually, these datasets may be insufficient for guiding management due to low spatio-temporal resolution, biased sampling, or large observational uncertainty. Integrated models provide an approach for assimilating multiple datasets in a coherent framework that can compensate for these deficiencies. While conventional integrated models have been used to assimilate count data with surveys of survival, fecundity, and harvest, they can also assimilate ecological surveys that have differing spatio-temporal regions and observational uncertainties. Motivated by independent aerial and ground surveys of lesser prairie-chicken, we developed an integrated modeling approach that assimilates density estimates derived from surveys with distinct sources of observational error into a joint framework that provides shared inference on spatio-temporal trends. We model these data using a Bayesian Markov melding approach and apply several data augmentation strategies for efficient sampling. In a simulation study, we show that our integrated model improved predictive performance relative to models for analyzing the surveys independently. We use the integrated model to facilitate prediction of lesser prairie-chicken density at unsampled regions and perform a sensitivity analysis to quantify the inferential cost associated with reduced surveyeffort.

Immunological Response to Respiratory Syncytial Virus Infection

Immunological response to Respiratory Syncytial Virus (RSV) involves multiple entities such as cellular, antibody, cytokine, chemokine, and Pattern Recognizing Receptors (PRRs) that are mobilized to subdue the airway and pulmonary effects of the virus. RSV predominantly involves the respiratory tract including the bronchioles and the lungs in addition to the upper respiratory passage. Although RSV afflicts all age groups predominantly in winter, severe infection is more common in the very young and in older individuals, i.e., 60-65 years of age. Recently, vaccines and monoclonal antibodies have been developed as a preventive strategy for the vulnerable.

Re-enacting machine learning practices to enquire into the moral issues they pose

As the number of ethical incidents associated with Machine Learning (ML) algorithms increases worldwide, many actors are seeking to produce technical and legal tools to regulate the professional practices associated with these technologies. However these tools, generally grounded either on lofty principles or on technical approaches, often fail at addressing the complexity of the moral issues that ML-based systems are triggering. They are mostly based on a ‘principled’ conception of morality where technical practices cannot be seen as more than mere means to be put at the service of more valuable moral ends. We argue that it is necessary to localise ethical debates within the complex entanglement of technical, legal and organisational entities from which ML moral issues stem. To expand the repertoire of the approaches through which these issues might be addressed, we designed and tested an interview protocol based on the re-enactment of data scientists’ daily ML practices. We asked them to recall and describe the crafting and choosing of algorithms. Then, our protocol added two reflexivity-fostering elements to the situation: technical tools to assess algorithms’ morality, based on incorporated ‘ethicality’ indicators; and a series of staged objections to the aforementioned technical solutions to ML moral issues, made by factitious actors inspired by the data scientists’ daily environment. We used this protocol to observe how ML data scientists uncover associations with multiple entities, to address moral issues from within the course of their technical practices. We thus reframe ML morality as an inquiry into the uncertain options that practitioners face in the heat of technical activities. We propose to institute moral enquiries both as a descriptive method serving to delineate alternative depictions of ML algorithms when they are affected by moral issues and as a transformative method to propagate situated critical technical practices within ML-building professional environments.

FedEE: A Federated Graph Learning Solution for Extended Enterprise Collaboration

Today's business environment is characterized by uncertainty and competition, so the capability to adapt to the evolving era and unforeseen challenges is essential in business strategies. Recent studies on extended enterprise indicate that collaboration among different stakeholders is beneficial for surviving these unexpected changes. However, the barriers such as market uncertainty, privacy and trust concerns, and individual contribution evaluation limit the implementation and application of the extended enterprise concept. Federated learning (FL), in which multiple enterprise entities can use a shared model while retaining all training data locally, has emerged as a promising AI solution for accumulating insights from multiple stakeholders and providing collaborative decision-making. Furthermore, the enhanced privacy-protection benefits of FL remove the barriers to implementing extended enterprise collaboration. In particular, an FL central server manages the local updates of multiple enterprise entities (FL clients) and aggregates their contributions to improve the global model training. Meanwhile, to address the time-series graph learning problem in most business environments, we incorporate TCN (Temporal Convolutional Network), GCN (Graph Convolutional Neural Network) and GRU (Gated Recurrent Unit) architecture into FL to capture the temporal-spatial dependencies in individual data sources. Furthermore, we use traffic flow forecasting as the use case of our proposed framework to verify its effectiveness. Finally, the experimental results on a real traffic flow dataset and the comparison results with the state-of-the-art baseline methods show that our proposed solution achieves superior performance.

Game Theory Based Optimal Pricing Strategy for V2G Participating in Demand Response

Aggregators, as the interface of electric vehicles (EVs) and distribution system, are the market agents of EVs to participate in demand response (DR) and other balancing services. Aggregators can enlarge its benefit room from vehicle-to-grid (V2G) by providing flexible V2G price to EV users as long as they can secure a certain amount of V2G energy. There is a need to study Aggregator's V2G pricing strategy due to the fact that distribution companies, aggregators, and EV users have their own independent benefit maximization objectives as independent entities, and the decision variables of different entities interact and restrict each other. In this study, an optimal V2G pricing strategy for each aggregator is obtained by using Stackelberg game during the gaming of the involved multiple entities. The benefits of aggregator and EV users are set as the game factors, and benefit models of EV user's and aggregator's are established with consideration of the historical charging cost, and users' inconvenience cost. A simulation of V2G price for 750 EVs and 3 aggregators is carried out, and the impact of the market volume of EVs on each aggregator under this pricing strategy is studied. Simulation results show the feasibility of the proposed model and the interaction among the randomness of driving behavior and the market volume of EV groups, loading level of the distribution system, V2G strategies, as well as the DR capabilities.

Cooperative Power Bidding of Smart Community Grids With an Aggregator-Prosumer-Based Hierarchical Framework

The progressive development of electricity markets envisions the participation of smart community grids in energy trading, but it is still challenging to guarantee global optimality for uncertain power management of multiple entities under a hierarchical framework. To tackle the cooperative strategic bid-ding problem with uncertainties, this paper proposes a hierar-chical three-stage robust optimization (HTS-RO) method. With respect to the bilayer coordination framework involving an ag-gregator gathering multiple prosumers, a novel HTS-RO bidding model is firstly established relying on the canonical centralized two-stage RO model. In the first stage, the aggregator performs power bidding in the upper layer according to the derived power flow information of the shared buses from the lower-layer prosumers. Considering renewable-load uncertainties and pri-vacy preservation, each prosumer conducts its own robust scheduling via the second and third stages based on the bidding prices from the aggregator and submits its optimal results of the shared bus back to the aggregator. For the robust scheduling model of each prosumer, the second stage identifies the basic plans of the nominal scenario, and the third stage checks the fea-sibility of the basic plans under uncertainties. To solve the re-sulting intractable multi-level mixed-integer nonlinear pro-gramming, a tri-loop decomposition-based iterative algorithm is tailored to ensure that the distributed computation converges to the optimal solution with the overall profit, thereby achieving the equivalent split of the centralized optimization into a hierarchical manner. Numerical tests on generic smart community grids with multiple prosumers validate the applicability and superiority of the proposed HTS-RO method for power bidding.

SBOannotator: a Python Tool for the Automated Assignment of Systems Biology Ontology Terms.

The number and size of computational models in biology have drastically increased over the past years and continue to grow. Modeled networks are becoming more complex, and reconstructing them from the beginning in an exchangeable and reproducible manner is challenging. Using precisely defined ontologies enables the encoding of field-specific knowledge and the association of disparate data types. In computational modeling, the medium for representing domain knowledge is the set of orthogonal structured controlled vocabularies named Systems Biology Ontology (SBO). The SBO terms enable modelers to explicitly define and describe model entities, including their roles and characteristics. Here, we present the first standalone tool that automatically assigns SBO terms to multiple entities of a given SBML model, named the SBOannotator. The main focus lies on the reactions, as the correct assignment of precise SBO annotations requires their extensive classification. Our implementation does not consider only top-level terms but examines the functionality of the underlying enzymes to allocate precise and highly specific ontology terms to biochemical reactions. Transport reactions are examined separately and are classified based on the mechanism of molecule transport. Pseudo-reactions that serve modeling purposes are given reasonable terms to distinguish between biomass production and the import or export of metabolites. Finally, other model entities, such as metabolites and genes, are annotated with appropriate terms. Including SBO annotations in the models will enhance the reproducibility, usability, and analysis of biochemical networks. SBOannotator is freely available from https://github.com/draeger-lab/SBOannotator/. Supplementary data are available at Bioinformatics online.

Path-guided intelligent switching over knowledge graphs with deep reinforcement learning for recommendation

AbstractOnline recommendation systems process large amounts of information to make personalized recommendations. There has been some progress in research on incorporating knowledge graphs in reinforcement learning for recommendation; however, some challenges still remain. First, in these approaches, an agent cannot switch paths intelligently, because of which, the agent cannot cope with multi-entities and multi-relations in knowledge graphs. Second, these methods do not have predefined targets and thus cannot discover items that are closely related to user-interacted items and latent rich semantic relationships. Third, contemporary methods do not consider long rational paths in knowledge graphs. To address these problems, we propose a deep knowledge reinforcement learning (DKRL) framework, in which path-guided intelligent switching was implemented over knowledge graphs incorporating reinforcement learning; this model integrates predefined target and long logic paths over knowledge graphs for recommendation systems. Specifically, the designed novel path-based intelligent switching algorithm with predefined target enables an agent to switch paths intelligently among multi-entities and multi-relations over knowledge graphs. In addition, the weight of each path is calculated, and the agent switches paths between multiple entities according to path weights. Furthermore, the long logic path has better recommendation performance and interpretability. Extensive experiments with actual data demonstrate that our work improves upon existing methods.The experimental results indicated that DKRL improved the baselines of NDCG@10 by 3.7%, 9.3%, and 4.7%; of HR@10 by 12.39%, 20.8%, and 13.86%; of Prec@10 by 5.17%, 3.57%, 6.2%; of Recall@10 by 3.01%, 4.2%, and 3.37%. The DKRL model achieved more effective recommendation performance using several large benchmark data sets compared with other advanced methods.

Primary Pathology of the Parapharyngeal Space.

The radiologically defined parapharyngeal space is adistinct location on cross-sectional imaging and is often described based on its displacement or invasion by tumors or other pathologies in adjacent spaces; however, there are multiple primary pathologic entities of the parapharyngeal space, which are often forgotten. Recognizing that alesion is arising from the parapharyngeal space is important in generating an accurate differential diagnosis that will guide management.