Two-level Process Research Articles

Research on Memory Problems in Playing

The process of music performance is a two-level creation process. Music performance has two links, one is learning works, and the other is performing works. How to interpret the perfect works is a problem faced by many players. This paper objectively analyzes the causes and solutions of the problems in the performance of forgetting music through various research methods such as questionnaires and documents. This paper thinks that it is possible to try to find a solution to reduce the errors in performance and provide reference for the majority of students.

Step by step: A hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning

Recently, knowledge graph reasoning has sparked great interest in research community, which aims at inferring missing information in triples and provides critical support to various tasks (e.g., question answering and recommendation). To date, multi-hop reasoning is a dominant approach which infers the target answer by walking along the path connecting entities and relations, ensuring both accuracy and interpretability. However, in most knowledge graphs, there are multiple relations related to an identical entity, and multiple tail entities for an identical pair of head entity and relation. Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning. In order to deal with such an issue, this work presents a novel paradigm for knowledge graph reasoning by decomposing it to a two-level hierarchical decision process. We apply the hierarchical reinforcement learning framework which dismantles the task into a high-level process for relation detector and a low-level process for entity reasoning, respectively. In this way, the action space is effectively controlled where the policies can be optimized. The interactions between entity and relation decision enhance the rationality of reasoning. Moreover, we introduce a dynamic prospect mechanism for low-level policy where the information can guide us to a refined and improved action space, assisted by embedding based method. Our proposed model is evaluated on four benchmark datasets and the results validate its superiority over state-of-the-art baselines, showing the interpretability of reasoning process simultaneously.

Data Fusion in Earth Observation and the Role of Citizen as a Sensor: A Scoping Review of Applications, Methods and Future Trends

Recent advances in Earth Observation (EO) placed Citizen Science (CS) in the highest position, declaring their essential provision of information in every discipline that serves the SDGs, and the 2050 climate neutrality targets. However, so far, none of the published literature reviews has investigated the models and tools that assimilate these data sources. Following this gap of knowledge, we synthesised this scoping systematic literature review (SSLR) with a will to cover this limitation and highlight the benefits and the future directions that remain uncovered. Adopting the SSLR guidelines, a double and two-level screening hybrid process found 66 articles to meet the eligibility criteria, presenting methods, where data were fused and evaluated regarding their performance, scalability level and computational efficiency. Subsequent reference is given on EO-data, their corresponding conversions, the citizens’ participation digital tools, and Data Fusion (DF) models that are predominately exploited. Preliminary results showcased a preference in the multispectral satellite sensors, with the microwave sensors to be used as a supplementary data source. Approaches such as the “brute-force approach” and the super-resolution models indicate an effective way to overcome the spatio-temporal gaps and the so far reliance on commercial satellite sensors. Passive crowdsensing observations are foreseen to gain a greater audience as, described in, most cases as a low-cost and easily applicable solution even in the unprecedented COVID-19 pandemic. Immersive platforms and decentralised systems should have a vital role in citizens’ engagement and training process. Reviewing the DF models, the majority of the selected articles followed a data-driven method with the traditional algorithms to still hold significant attention. An exception is revealed in the smaller-scale studies, which showed a preference for deep learning models. Several studies enhanced their methods with the active-, and transfer-learning approaches, constructing a scalable model. In the end, we strongly support that the interaction with citizens is of paramount importance to achieve a climate-neutral Earth.

Complete convergence theorem for a two-level contact process

We study a two-level contact process. We think of fleas living on a species of animals. The animals are a supercritical contact process in $\mathbb{Z}^d$. The contact process acts as the random environment for the fleas. The fleas do not affect the animals, give birth at rate $\mu$ when they are living on a host animal, and die at rate $\delta$ when they do not have a host animal. The main result is that if the contact process is supercritical and the fleas survive then the complete convergence theorem holds. This is done using a block construction so as a corollary we conclude that the fleas die out at their critical value.

Evidence and consensus-based clinical practice guidelines for management of overweight and obesity in midlife women: An AIIMS-DST initiative.

Evidence and consensus-based clinical practice guidelines for management of overweight and obesity in midlife women: An AIIMS-DST initiative.

Early Development of Neural Speech Encoding Depends on Age but Not Native Language Status: Evidence From Lexical Tone.

We investigated the development of early-latency and long-latency brain responses to native and non-native speech to shed light on the neurophysiological underpinnings of perceptual narrowing and early language development. Specifically, we postulated a two-level process to explain the decrease in sensitivity to non-native phonemes toward the end of infancy. Neurons at the earlier stages of the ascending auditory pathway mature rapidly during infancy facilitating the encoding of both native and non-native sounds. This growth enables neurons at the later stages of the auditory pathway to assign phonological status to speech according to the infant's native language environment. To test this hypothesis, we collected early-latency and long-latency neural responses to native and non-native lexical tones from 85 Cantonese-learning children aged between 23 days and 24 months, 16 days. As expected, a broad range of presumably subcortical early-latency neural encoding measures grew rapidly and substantially during the first two years for both native and non-native tones. By contrast, long-latency cortical electrophysiological changes occurred on a much slower scale and showed sensitivity to nativeness at around six months. Our study provided a comprehensive understanding of early language development by revealing the complementary roles of earlier and later stages of speech processing in the developing brain.

Personality-Based Adaptation for Teamwork in Game Agents

This paper presents a novel learning framework to provide computer game agents the ability to adapt to the player as well as other game agents. Our technique generally involves a personality adaptation module encapsulated in a reinforcement learning framework. Unlike previous work in which adaptation normally involves a decision process on every single action the agent takes, we introduce a two-level process whereby adaptation only takes place on an abstracted actions set which we coin as agent personality. With the personality defined, each agent will then take actions according to the restrictions imposed in its personality. In doing so, adaptation takes place in appropriately defined intervals in the game, without disrupting or slowing down the game constantly with intensive decision-making computations, hence improving enjoyment for the player. Moreover, by decoupling adaptation from action selection, we have a modular adaptive system that can be used with existing action planning methods. With an actual typical game scenario that we have created, it is shown that a team of agents using our framework to adapt towards the player are able to perform better than a team with scripted behavior. Consequently, we also show the team performs even better when adapted towards each other.

Dynamic expansion of flexible capacity with and without pricing coordination

This research presents an optimal multi-period dynamic capacity expansion framework for a shared flexible capacity producing two products. The framework allows modeling coordinated and non-coordinated capacity and marketing decisions under demand uncertainties. In our two-level decision process, the upper level considers the strategic capacity expansion and formulates multi-period uncertainty as a stochastic dynamic programming (DP) problem, and the lower level considers each period’s pricing and capacity allocation decisions in response to available capacity and market demand. We compare capacity expansion decisions under centralized pricing schemes that coordinate the pricing decisions of different products based on the overall interest of the firm and the decentralized pricing schemes based on the interests of individual product managers. Numerical results show that adopting a decision coordination strategy may help firms to maintain flexibility and reduce capacity expansion investment while enhancing overall profitability.

Sexually Transmitted Infections in U.S. Military Women: A Scoping Review 2000–2018

High rates of sexually transmitted infections (STIs) have been documented among U.S. military servicemembers. The purpose of this scoping review is to evaluate the literature to determine what is known about the risk factors, preventive measures, and health outcomes regarding STIs among active duty servicewomen. A search of six bibliographic databases and the grey literature identified articles published from January 1, 2000, to December 31, 2018. A two-level review process was used to evaluate the inclusion of articles. Fifty-six articles were included. The majority of studies (n=47) were descriptive (95%). The primary STIs of focus were chlamydia (66%) and gonorrhea (38%), with a lesser focus on herpes simplex virus 1 and 2 (17%) and syphilis (11%). There were no studies on chancroid or pubic lice. Chlamydia and gonorrhea were highly prevalent. Age, race, and gender were nonmodifiable risk factors, whereas behaviors, beliefs, socioeconomic level, marital status, and concomitant or repeat infections were modifiable risk factors. Educational programs and studies evaluating efficacious STI prevention methods were lacking. STI diagnoses occurred in servicewomen at their home stations as well as in deployed settings. STIs remain an ongoing public health challenge with insufficient research to guide military and health care leaders. Future research should focus on prospective designs that leverage identified risk factors and at-risk populations where the most impact can be made to promote reproductive health.

A comprehensive micromechanical and experimental study of the electrical conductivity of polymeric composites incorporating carbon nanotube and carbon fiber

The present study proposes a micromechanical model composed of a two-level homogenization process for predicting the electrical conductivity of polymeric composites incorporating carbon nanotube (CNT) and carbon fiber (CF). The curviness of the CNT and the interfacial resistivity between the polymer matrix and the CNT are considered in the micromechanical model. A series of numerical studies with model parameters are carried out, after which a genetic algorithm is applied to determine the optimized model parameters. The electrical and morphological properties of the composites are experimentally evaluated according to the content of CNT and CF. To verify the predictive capability of the proposed model, the present prediction is compared with that obtained from experiments. The proposed model combined with the genetic algorithm is found to closely simulate the effective electrical conductivity of the composites, as evidenced by the credible accuracy to the experimentally measured values.

Nested Alternating - Triangular Incomplete Factorization Methods

We consider several versions of incomplete nested factorization methods for solving the large systems of linear algebraic equations (SLAEs) with sparse matrices which arise in grid approximations of the multi-dimensional boundary value problems. Our approach is based on the two-level iterative process in the Krylov subspaces in 3D case. Corresponding hierarchical incomplete factorization is applied to the block tridiagonal matrix structure. At the upper level, the diagonal blocks correspond to 2D grid subproblems which are factorized in the line-by-line framework. Instead of the low and upper triangular matrix factors, the alternating triangular matrices are used, which allows to apply the parallel counter sweeping approaches. The improvement of preconditioners is made by means of generalized compensation principles. To solve SLAE iterative conjugate direction methods in Krylov subspaces are applied. The efficiency of the proposed methods are demonstrated on the set of representative test problems.

Analysis of a COVID-19 Prescreening Process in an Outpatient Clinic at a University Hospital during the COVID-19 Pandemic.

BackgroundTo minimize nosocomial infection against coronavirus disease 2019 (COVID-19), most hospitals conduct a prescreening process to evaluate the patient or guardian of any symptoms suggestive of COVID-19 or exposure to a COVID-19 patient at entrances of hospital buildings. In our hospital, we have implemented a two-level prescreening process in the outpatient clinic: an initial prescreening process at the entrance of the outpatient clinic (PPEO) and a second prescreening process is repeated in each department. If any symptoms or epidemiological history are identified at the second level, an emergency code is announced through the hospital's address system. The patient is then guided outside through a designated aisle. In this study, we analyze the cases missed in the PPEO that caused the emergency code to be applied.MethodsAll cases reported from March 2020 to April 2021 were analyzed retrospectively. We calculated the incidence of cases missed by the PPEO per 1,000 outpatients and compared the incidence between first-time hospital visitors and those visiting for the second time or more; morning and afternoon office hours; and days of the week.ResultsDuring the study period, the emergency code was applied to 449 cases missed by the PPEO. Among those cases, 20.7% were reported in otorhinolaryngology, followed by 11.6% in gastroenterology, 5.8% in urology, and 5.8% in dermatology. Fever was the most common symptom (59.9%), followed by cough (19.8%). The incidence of cases per 1,000 outpatients was significantly higher among first-time visitors than among those visiting for the second time or more (1.77 [confidence interval (CI), 1.44–2.10] vs. 0.59 [CI, 0.52–0.65], respectively) (P < 0.001).ConclusionFever was the most common symptom missed by the PPEO, and otorhinolaryngology and gastroenterology most frequently reported missed cases. Cases missed by the PPEO were more likely to occur among first-time visitors than returning visitors. The results obtained from this study can provide insights or recommendations to other healthcare facilities in operating prescreening processes during the COVID-19 pandemic.

Fuzzy Relational Matrix Factorization and Its Granular Characterization in Data Description

This article is concerned with a problem of relational factorization which engages fuzzy relational calculus. It forms an interesting alternative to the method of nonnegative matrix factorization that has been commonly discussed and found in numerous applications. The relational factorization takes original <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -dimensional data located in the unit hypercube and factorizes it into data of lower dimensionality and some fuzzy relations. Owing to the logic nature of processing delivered by relational calculus, the dimensionality reduction exhibits transparency as the reduction mechanism itself is described in terms of logic expressions. Two types of factorizations mechanisms are investigated by using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s–t</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t–s</i> composition operators where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> are triangular norms and conorms, respectively. A two-level process of factorization is designed. A gradient-based learning scheme is developed. The quantification of the performance of the factorization process is realized by bringing a concept of information granularity: The obtained fuzzy relations are formed based on granular constructs and the quality of the produced factorization is assessed in terms of the coverage and specificity of the obtained granular results. A collection of experiments is included to present the performance of factorization and its parametric analysis. In addition, the proposed algorithm comes with sound interpretability in terms of both the structure of the model and an intuitive meaning of the fuzzy relations being the result of factorization.

Isofunctional Clustering and Conformational Analysis of the Arsenate Reductase Superfamily Reveals Nine Distinct Clusters.

Arsenate reductase (ArsC) is a superfamily of enzymes that reduce arsenate. Due to active site similarities, some ArsC can function as low-molecular weight protein tyrosine phosphatases (LMW-PTPs). Broad superfamily classifications align with redox partners (Trx- or Grx-linked). To understand this superfamily's mechanistic diversity, the ArsC superfamily is classified on the basis of active site features utilizing the tools TuLIP (two-level iterative clustering process) and autoMISST (automated multilevel iterative sequence searching technique). This approach identified nine functionally relevant (perhaps isofunctional) protein groups. Five groups exhibit distinct ArsC mechanisms. Three are Grx-linked: group 4AA (classical ArsC), group 3AAA (YffB-like), and group 5BAA. Two are Trx-linked: groups 6AAAAA and 7AAAAAAAA. One is an Spx-like transcriptional regulatory group, group 5AAA. Three are potential LMW-PTP groups: groups 7BAAAA, and 7AAAABAA, which have not been previously identified, and the well-studied LMW-PTP family group 8AAA. Molecular dynamics simulations were utilized to explore functional site details. In several families, we confirm and add detail to literature-based mechanistic information. Mechanistic roles are hypothesized for conserved active site residues in several families. In three families, simulations of the unliganded structure sample specific conformational ensembles, which are proposed to represent either a more ligand-binding-competent conformation or a pathway toward a more binding-competent state; these active sites may be designed to traverse high-energy barriers to the lower-energy conformations necessary to more readily bind ligands. This more detailed biochemical understanding of ArsC and ArsC-like PTP mechanisms opens possibilities for further understanding of arsenate bioremediation and the LMW-PTP mechanism.

Using nudges to promote physical activity and to reduce sedentary behaviour in the workplace: a scoping review protocol

IntroductionPhysical inactivity and sedentary behaviour are associated with numerous health problems and increasing risks of premature morbidity and mortality. Workplace health promotion with a focus on increasing physical activity (PA)...

DOCTRINE CREATION – THE STAGE OF THE SOCIAL (SOCIALLY CONSCIOUS) MANAGEMENT PROCESS OF SOCIAL BEHAVIOUR OF THE SUBJECTS OF SOCIAL RELATIONS

The active formation in Ukraine of a block of long-term planning program documents (doctrines, strategies, etc.) in various areas of public relations necessitates the creation of a doctrinal legal basis. At present, the issue of defining the role, place and significance of doctrine creation as a stage in the process of managing social behaviour, as well as at the level of public (socially conscious) management has not been studied. The purpose of this study is to examine the process of managing the social behaviour of the subjects of public relations at the level of public (socially conscious) management; the role, place and content of doctrine creation as a stage of the studied process.The methodological basis of the study is constituted by a set of methods and techniques of scientific knowledge: hermeneutic, analysis and synthesis, logical-semantic analysis, system-structural, etc. The author notes that each process consists of several stages that differ in content and mechanisms, but when it comes to the process of managing social behaviour, it should be borne in mind that this process takes place on two levels: the level of social (socially conscious) management and levels of state (will) government. The understanding of the two-level research process was chosen as the research paradigm, according to which the sequence of stages, steps and their content were analyzed as the components of the process of managing social behaviour of public relations at the level of public (socially conscious) management and determining the place, role and content of doctrine as stages of this process, which also consists of a certain sequence of steps. As a result of the research, the author’s vision of using the terms “public (socially-conscious) management”, “social rule-making”, “social ordering”, “social rule-consciousness”, etc. was formulated. It is outlined that doctrine creation is an optional stage of the research process, and is a source of formation of social rule consciousness (consciousness in the field of social management of social behaviour) of the subjects of public relations and lawmakers at the stage of authorization, which is also referred to the optional stage of the studied process. The author identifies that “social diagnostics” is a component of the process of doctrine creation. The practical significance of the study is to develop proposals for improving the preparatory, normative design activities in the formation of program documents and the creation of their doctrinal basis.

Recurrence of cervical artery dissection: protocol for a systematic review

IntroductionCervical artery dissection, including carotid and vertebral artery dissection, is an important cause of stroke in the young. Risk of developing cervical artery dissection has been associated with physical activity in various forms and has been presumed to be related to minor trauma and mechanical stretching of the cervical arteries. This systematic review will aim to synthesise data on the risk of recurrent cervical artery dissection after an initial dissection. This information may be applied to further understand the natural history of this disease, and potentially to help direct evidence-based discussions on safe return to activity after dissection.Methods and analysisA broad search of multiple electronic databases (Medline, Embase, Cochrane Central Register of Controlled Trials and Web of Science) will be conducted to identify studies published as of 13 November 2019, examining all-comers with cervical artery dissection observed over time. Studies will be screened by two independent reviewers in a two-level process to determine eligibility for inclusion. Data will be pooled from eligible articles and the main outcome of recurrent cervical artery dissection at 5 years will be determined using quantitative analysis.Ethics and disseminationEthics approval is not necessary as no primary data are being collected. The information will be disseminated in the form of a systematic review article which will be submitted to a peer-reviewed medical journal.PROSPERO registration numberCRD42020166105.

Risk factor identification in cystic fibrosis by flexible hierarchical joint models.

Cystic fibrosis (CF) is a lethal autosomal disease hallmarked by respiratory failure. Maintaining lung function and minimizing frequency of acute respiratory events known as pulmonary exacerbations are essential to survival. Jointly modeling longitudinal lung function and exacerbation occurrences may provide better inference. We propose a shared-parameter joint hierarchical Gaussian process model with flexible link function to investigate the impacts of both demographic and time-varying clinical risk factors on lung function decline and to examine the associations between lung function and occurrence of pulmonary exacerbation. A two-level Gaussian process is used to capture the nonlinear longitudinal trajectory, and a flexible link function is introduced to the joint model in order to analyze binary process. Bayesian model assessment criteria are provided in examining the overall performance in joint models and marginal fitting in each submodel. We conduct simulation studies and apply the proposed model in a local CF center cohort. In the CF application, a nonlinear structure is supported in modeling both the longitudinal continuous and binary processes. A negative association is detected between lung function and pulmonary exacerbation by the joint model. The importance of risk factors, including gender, diagnostic status, insurance status, and BMI, is examined in joint models.

Data-Dependent Conditional Priors for Unsupervised Learning of Multimodal Data.

One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the prior for the latent code in the ancestral sampling procedure for data generations. In this paper, we propose a novel formulation of variational autoencoders, conditional prior VAE (CP-VAE), with a two-level generative process for the observed data where continuous and a discrete variables are introduced in addition to the observed variables . By learning data-dependent conditional priors, the new variational objective naturally encourages a better match between the posterior and prior conditionals, and the learning of the latent categories encoding the major source of variation of the original data in an unsupervised manner. Through sampling continuous latent code from the data-dependent conditional priors, we are able to generate new samples from the individual mixture components corresponding, to the multimodal structure over the original data. Moreover, we unify and analyse our objective under different independence assumptions for the joint distribution of the continuous and discrete latent variables. We provide an empirical evaluation on one synthetic dataset and three image datasets, FashionMNIST, MNIST, and Omniglot, illustrating the generative performance of our new model comparing to multiple baselines.

Iterative Solution of Saddle-Point Systems of Linear Equations

The paper considers preconditioned iterative methods in Krylov subspaces for solving systems of linear algebraic equations (SLAEs) with a saddle point arising from grid approximations of threedimensional boundary-value problems of various types describing filtration flows of a two-phase incompressible fluid. A comparative analysis of up-to-date approaches to block preconditioning of SLAEs under consideration, including issues of scalable parallelization of algorithms on multiprocessor computing systems with distributed and hierarchical shared memory using hybrid programming tools, is presented. A regularized Uzawa algorithm using a two-level iterative process is proposed. Results of numerical experiments for the Dirichlet and Neumann model boundary-value problems are provided and discussed. Bibliography: 15 titles.