Different Types Of Dependencies Research Articles

Farewell to the requirement for character independence: phylogenetic methods to incorporate different types of dependence between characters.

This paper discusses methods to take into account interactions between characters, in the context of parsimony analysis. These interactions can be in the form of some characters becoming inapplicable given certain states of other, primary characters; in the form of only certain states being allowed in some characters when a given state or set of states occurs for other characters; or in the form of transformation costs in some character being higher or lower when other characters have certain states or transformations between states. Character-state reconstructions and evaluation of trees under the assumption of independence may easily lead to ancestral assignments that violate elementary rules of biomechanics, well-established theories relating form and function or ideas about character co-variation. An obvious example is reconstructing an ancestral bird as wingless and flying at the same time; another is reconstructing a protein-coding gene as having a stop codon in some ancestors. If the characters are optimized independently, such chimeric ancestral reconstructions can occur even when no terminal displays the impossible combination of states. A set of conventions (implemented via new TNT commands and options) allows the definition of complex rules of interaction. By recoding groups of characters with proper step-matrix costs (and excluding impossible combinations from the set of permissible states), it is possible to find the ancestral reconstructions that maximize homology (and thus the degree to which similarities can be explained by common ancestry), within the constraints imposed by the rules specified by the user. We expect that considerations of biomechanics, functional morphology and natural history will be a source of many theories on possible character dependences, and that the present implementation will encourage users to take the possibility of character dependences into account in their phylogenetic analyses.

DEVELOPMENT OF A SOLUTION FOR AUTOMATIC TRACKING OF THE STATUS OF TASK DEPENDENCES IN SMARTSHEET

The article proposes a solution to automate the dependencies completion tracking in Smartsheet for a specific task. The solution allows tracking different types of dependencies for a task: completing direct dependencies from other tasks and completing dependencies for all of its ancestors. The algorithm of the solution, Smartsheet’s functions for its implementation, and the screen forms of the obtained results are given.

Differences of Mean Dependency Distances of English Essays Written by Learners of Different Proficiency Levels

This study investigates the differences in the mean dependency distances (MDDs) of the English essays in a learner corpus, focusing on the different proficiency levels of learners, and the different dependency types. This study is based on the following three assumptions. Firstly, the MDDs of learners' production increase as proficiency levels increase. Secondly, there is an upper limit over which MDDs do not exceed, as predicted by the Dependency Distance Minimization principle. Finally, different types of dependencies show different tendencies across learners of different proficiency levels. This study attempts to verify these assumptions with substantial learner corpus data, categorized into subcorpora according to learner proficiency. Corpus analyses yield results that support these assumptions. These results are expected to constitute a prerequisite for employing the MDD of an individual learner's production to evaluate his or her proficiency level.

AUTOMATE TASK’ DEPENDENCIES COMPLETION TRACKING IN SMARTSHEET

Smartsheet is the well-known project management application. It helps managers to develop project plans and track their execution. When project’s tasks have many dependencies, a manager often has to scroll the plan up and down for visual assessment of completion statuses each of them. It needs many efforts and time. 
 The article proposes a solution to automate the dependencies completion tracking in Smartsheet for a specific task. The solution allows tracking different types of dependencies for a task: completing direct dependencies from other tasks and completing dependencies for all of its ancestors. The algorithm of the solution, Smartsheet’s functions for its implementation, and the screen forms of the obtained results are given.

Joint optimization of production and condition-based maintenance scheduling for make-to-order manufacturing systems

In make-to-order manufacturing systems, the production plan is made according to randomly fluctuating customer orders, therefore, the workload distributed to production machines fluctuates with customer orders. The workload that a production machine undertakes influences its deterioration process and maintenance. Besides, different types of dependencies among production machines also influence the production and maintenance scheduling of manufacturing systems. Existing studies have not paid enough attention to the joint optimization of production and maintenance for make-to-order manufacturing systems considering different types of dependencies. In this paper, first, customer orders are predicted using Autoregressive Integrated Moving Average (ARIMA) or Seasonal ARIMA (SARIMA) according to their characteristics. Second, the model of daily working hours is proposed to represent the influence of customer orders on the deterioration process of production machines. Third, the reliability and imperfect maintenance models of production machines considering three types of dependencies are proposed, and the optimal maintenance time window (MTW) for opportunistic maintenance is obtained in order to minimize the maintenance cost. Fourth, the mathematical model of the entire cost of a new production line construction, covering maintenance cost, operators’ salary, and construction cost, is proposed, and the optimal construction time is obtained in order to minimize the entire cost. Finally, a real use case of the manufacturing system for the shell of air-conditioning compressor is given to illustrate the feasibility and applicability of the proposed model and method. The originality of this work lies in the consideration of the influence of customer orders on the deterioration process of production machines and the modelling of different types of dependencies among production machines in a single model.

Violation of Conditional Independence in the Many-Facets Rasch Model

ABSTRACT Estimation of parameters for the many-facets Rasch model requires that conditional on the values of the facets, such as person ability, item difficulty, and rater severity, the observed responses within each facet are independent. This requirement has often been discussed for the Rasch models and 2PL and 3PL models, but it becomes more complex with more facets. To show how violation of conditional independence may be exhibited, three scenarios with different types of dependency are developed: (a) raters rating the same work, (b) a residual ability shared by two tasks, and (c) score on one task dependent on observed score on a previous task.

A multivariate nonlinear regression model for the resistance power of a light rail vehicle

In Turkey, 20% of energy use caused by transportation. Light rail transportation has developing nowadays. However, it is very important to issue having information about the energy consumption of the light rail vehicle according to different both vehicle and railway situations. 
 In Turkey, approximately 20% of the energy expended is spent on transportation. Light rail transportation technology is still in an evolving process. In this development process, it is crucial to have information about the energy consumption of the light rail vehicle according to the different situations of both the vehicle and the railway.
 It is necessary to predict the power losses that will occur under different driving conditions sensitively to ensure energy efficiency in light rail systems. The most important of these power losses is the resistance loss caused by contact with the route. Resistance loss is dependent multiple environmental conditions. The most important of these conditions can be listed as the weight of the light rail vehicle, the instantaneous speed of the vehicle, the curve of the route, the ramp slope of the route and the friction force arising from these conditions. Resistance loss is proportional and linearly dependent to some of these variables while others show reverse or nonlinear dependence. Due to these different types of dependencies, it is seen that a single multivariate nonlinear model is needed to explain the loss of resistance in all different conditions. In this study, a new and accurate model for resistance losses has been developed by fitting numerical values obtained from different scenarios to multivariate nonlinear regression model.

Business management in modern economic management conditions

The paper presents indicators of modern business management, based on the current economic situation of its management. When selecting indicators, the emphasis is placed on the last stage of management evolution, which is characterized by the primary role of scientific and technical achievements in the development of the economy, including its commercial sector, and, accordingly, such resulting and factor indicators as changes in income and expenses, direct and indirect ratio of income and profit to expenditures on human capital, investment capital, and innovation capital are taken into account. The construction of these dependencies between changes in income and expenses is carried out through a combination of different types of dependencies (additive, multiplicative, multiple), which help to identify the degree of influence of each factor indicator on the resulting indicators and take actions to maximize the result and minimize business costs using the computational and constructive method in interaction with individual elements of mathematical analysis. The material is useful for entrepreneurs, regardless of their type of activity and organizational and legal status, educational and scientific figures, students of higher and secondary specialized institutions of economic and managerial programs, specialties and profiles.

The Why, How, and When of Representations for Complex Systems

Complex systems, composed at the most basic level of units and their interactions, describe phenomena in a wide variety of domains, from neuroscience to computer science and economics. The wide variety of applications has resulted in two key challenges: the generation of many domain-specific strategies for complex systems analyses that are seldom revisited, and the compartmentalization of representation and analysis ideas within a domain due to inconsistency in complex systems language. In this work we propose basic, domain-agnostic language in order to advance toward a more cohesive vocabulary. We use this language to evaluate each step of the complex systems analysis pipeline, beginning with the system under study and data collected, then moving through different mathematical frameworks for encoding the observed data (i.e., graphs, simplicial complexes, and hypergraphs), and relevant computational methods for each framework. At each step we consider different types of dependencies; these are properties of the system that describe how the existence of an interaction among a set of units in a system may affect the possibility of the existence of another relation. We discuss how dependencies may arise and how they may alter the interpretation of results or the entirety of the analysis pipeline. We close with two real-world examples using coauthorship data and email communications data that illustrate how the system under study, the dependencies therein, the research question, and the choice of mathematical representation influence the results. We hope this work can serve as an opportunity for reflection for experienced complex systems scientists, as well as an introductory resource for new researchers.

Dependence Types in a Binarized Precipitation Network

AbstractIn a network of binarized precipitation (i.e., wet or dry value), the connection or dependence between each pair of nodes can occur following one or more of the following conditions: wet‐wet, dry‐dry, wet‐dry, or dry‐wet. Here, we firstly investigate the different types of dependence, year by year, within a precipitation network of binarized variables. We compare the sample estimate of the probability of co‐occurrence (or occurrence with a lag time within ±3 days) of each of the four possible combinations with respect to the correspondent confidence interval in hypothesis of independence. We develop a procedure to efficiently assess the dependence behavior of all couples of nodes within the network and apply the methodology to a network of rain gauges covering Europe and north Africa.

Modeling and Analyzing Supporting Systems for Smart Manufacturing Systems with Stochastic, Technical and Economic Dependences

Smart manufacturing systems are triggerring the next industrial revolution.They are intended to be collaborative manufacturing systems that respond in real time to meet the system’s changing demands and conditions. Different types of dependencies among system components are introduced to enable this and to improve system performance, including structural, stochastic, technical and economic dependences. Supporting systems are also introduced to this aim, through specified interfaces. In this paper, the role of maintenance policy, spare part inventory and buffer size as supporting systems of smart systems is considered. Load-sharing dependence, adaptive control with feedback and economic dependence are specifically considered, and their effect is studied via Monte Carlo simulation. Results show that smart systems with properly designed supporting systems have undoubtedly increased system complexity and dependencies, but can indeed increase availability and production volume, and system efficiency overall, with total cost reduced.

ARBITRARY FUNCTIONAL GLIVENKO-CANTELLI CLASSES AND APPLICATIONS TO DIFFERENT TYPES OF DEPENDENCE

Using a general strong law of large number proved by Sangare and Lo (2015) and the entropy numbers, we provide functional Glivenko-Cantelli (GC) classes for arbitrary stationary real-valued random variables (rrv's). Next, the general results are particularized for different types of dependence (association, $\phi$-mixing, in particular) and compared with available results in the literature.

Surface wave investigation and phenomenological analysis: Application on in vivo human cutaneous tissue

The wave phenomenon in free surface media stems from the propagation of mode grouping. Due to the nature of propagation in a given medium, this phenomenon expresses different types of dependence on the medium's properties and represents its mechanical admittance. In contrast with body wave propagation, dependencies related to surface propagation in a medium can be described by spatial-temporal characteristics. These characteristics can be obtained by performing appropriate experiments and do not require prior knowledge of the physical properties of the medium. In this study, we propose an original surface wave investigation and a phenomenological analysis approach adapted to the mechano-bio-structural states evaluation of in vivo human skin. Two objectives are sought with the method proposed: the first concerns the development of a non-invasive device for generating and tracking surface waves in human skin called Free-Skin-Surface-Wave (FSSW); the second concerns the adaptation of the Multi-Chanel Analysis of Surface Waves (MASW) method to evaluate the mechano-bio-structural states of human cutaneous tissue in vivo on the basis of the propagating phenomena observed. As an illustration of the proposed method application, we have done an in vivo evaluation, on intern-forearm of female volunteers population. In addition, we proposed a study of the aging effect and a comparison with ultrasound B-Mode technique, to validate the method sensitivity to follow the mechano-morphological properties of the in vivo human skin. In this study, our medium of application was human skin in vivo, but it is conceivable to extend this application to other soft biological media.

The Posterior Perforated Substance: A Brain Mystery Wrapped in an Enigma.

There is a dearth of published information on the posterior perforated substance as compared to the anterior perforated substance. We managed to glean facts about the posterior perforated substance that can serve as a landmark for surgical operations in the adjacent regions of the midbrain and the vessels passing through it. Moreover, the posterior perforated substance contains the interpeduncular nucleus responsible for the mental state of the individual. 1) To describe the topography of the blood vessels supplying the posterior perforated substance area from the surgical point of view; 2) to investigate the functions of the interpeduncular nucleus. We assembled and analyzed results from source databases by Elsevier, NCBI MedLine, Scopus, Scholar. Google and Embase. Each article was studied in detail for practically useful information about the posterior perforated substance. The P1-segment perforating branches of the posterior cerebral artery supply the posterior perforated substance. This area is especially vulnerable in the case of vascular pathologies. The posterior communicating artery can block the surgeon's view and impede maneuverability of the tool in the area of the posterior perforated substance, which may be addressed using the separation technique, which can lead to positive results. In addition, the medial habenula-interpeduncular nucleus in the posterior perforated substance is associated with various addictions and psychiatric conditions. The posterior perforated substance area is of great interest for surgical interventions. Future studies of the interpeduncular nucleus anticipate the development of drugs to affect different types of dependencies and some mental diseases.

On parameter estimation approaches for predicting disease transmission through optimization, deep learning and statistical inference methods

In this paper, we consider compartmental disease transmission models and discuss the importance of determining model parameters that provide an insight into disease transmission and prevalence. After a brief review and comparison of the performance of some heuristic approaches, the paper introduces three approaches including an optimization approach, a physics informed deep learning and a statistical inference method to estimate parameters and analyse disease transmission. The deep learning framework utilizes the hidden physics of infectious diseases and infer the latent quantities of interest in the model by approximating them using deep neural networks. The performance of the deep learning method is validated against representative small and big data sets corresponding to a well-known benchmark example and the results indicate that deep learning is a viable candidate to determine model parameters. The paper also presents the need for researchers to understand different types of dependencies exhibited in a typical data set and discovering the most appropriate approaches for statistical inference. Specifically, in this work we apply a time-series inferential method with a variety of statistical models. Our results indicate the efficiency and importance of statistical inference methods for researchers to understand and analyse time-series data to make confident predictions.

On the Equivalence Between Abstract Dialectical Frameworks and Logic Programs

AbstractAbstract Dialectical Frameworks (ADFs) are argumentation frameworks where each node is associated with an acceptance condition. This allows us to model different types of dependencies as supports and attacks. Previous studies provided a translation from Normal Logic Programs (NLPs) toADFs and proved the stable models semantics for a normal logic program has an equivalent semantics to that of the correspondingADF. However, these studies failed in identifying a semantics forADFs equivalent to a three-valued semantics (as partial stable models and well-founded models) forNLPs. In this work, we focus on a fragment ofADFs, called Attacking Dialectical Frameworks (ADF+s), and provide a translation fromNLPs toADF+s robust enough to guarantee the equivalence between partial stable models, well-founded models, regular models, stable models semantics forNLPs and respectively complete models, grounded models, preferred models, stable models forADFs. In addition, we define a new semantics forADF+s, calledL-stable, and show it is equivalent to theL-stable semantics forNLPs.

Liability of foreignness and anti-corruption reporting in an emerging market: The case of Turkish listed companies

Abstract: This study examines the association between different types of dependency on resources and/or pressures from the international community and the reporting practices on the fight against corruption of companies in an emerging country setting, that of Turkey. More specifically, we focus on the influence of multinationality, cross-listing, and membership of the United Nations Global Compact on this type of reporting. We use ordinal regression analysis to explore the association between the three factors mentioned above and anti-corruption reporting for a sample of Turkish firms on the Borsa Istanbul 100 index, while controlling for some other factors likely to influence anti-corruption reporting. Findings show a low level of reporting. They also suggest that companies with their shares cross-listed and companies which are members of the Uited Nations Global Compact do present higher levels of anti-corruption reporting than their counterparts.

Identification of Dependencies in Task Allocation during Distributed Agile Software Development

There is a growing interest for Distributed Agile Software Development (DASD) in software industry due to multiple benefits such as availability of resources, low development cost, changeability and customer satisfaction. Carrying out DASD poses a challenge of effective task allocation, which requires active coordination between dispersed teams. Effective coordination can be achieved by identifying and managing dependencies in DASD environment. To understand the task allocation complexity due to dispersed teams at different sites, we identify different types of dependencies in DASD. These dependencies must be recognized as they have a profound influence on software product achievement using DASD. Identification and understanding them support management to timely recognize different types of issues that can cause delay, interruptions or even cancellation of Agile Sprints. DASD being an emerging practice, their understanding also leverages methodical task allocation that can optimize on quality concerns such as cost of development and quality of product.

Does Resource Diversity Confer Organizational Autonomy In Arts Organizations? Extending Resource Dependence Theory

Resource dependence theory holds that organizations adopt a variety of strategies to manage their dependence on resources drawn from the external environment. Most research on resource dependence theory, however, has focused on empirical contexts in which the organization is dependent upon a dominant single external source of resources. We have little understanding of which dependencies take priority over others in contexts where multiple critical dependencies co-exist. More importantly, we don't know how different types of dependencies differentially affect organizational autonomy. We address these questions in this study of over six hundred performing arts organizations. Our analysis reveals a powerful configurational effect in which different categories of resources differentially affect organizational action. We also demonstrate how organizations strategically manage their portfolio of resources in an effort to maximize their autonomy.

Flexible dependence modeling using convex combinations of different types of connectivity structures

There is a great deal of literature regarding use of non-geographically based connectivity matrices or combinations of geographic and non-geographic structures in spatial econometrics models. We explore alternative approaches for constructing convex combinations of different types of dependence between observations. Pace and LeSage (2002) as well as Hazır et al. (2016) use convex combinations of different connectivity matrices to form a single weight matrix that can be used in conventional spatial regression estimation and inference. An example for the case of two weight matrices, W1,W2 reflecting different types of dependence between a cross-section of regions, firms, individuals etc., located in space would be: Wc=γ1W1+(1−γ1)W2,0≤γ1≤1. The matrix Wc reflects a convex combination of the two weight matrices, with the scalar parameter γ1 indicating the relative importance assigned to each type of dependence. We explore issues that arise in producing estimates and inferences from these more general cross-sectional regression relationships in a Bayesian framework. We propose two procedures to estimate such models and assess their finite sample properties through Monte Carlo experiments. We illustrate our methodology in an application to CEO salaries for a sample of nursing homes located in Texas. Two types of weights are considered, one reflecting spatial proximity of nursing homes and the other peer group proximity, which arise from the salary benchmarking literature.