Micro-macro problem in sociology: Towards a solution

The researcher reviewed the works of some major micro-theorists and deduced solutions to the problem. The researcher deduced one of the solutions from the works of Randall Collins which is named aggregation model. Similarly, another model was deduced from the works of James Coleman called coordination model. These models show a movement from micro-observations made or data collected to the macro level where generalisations are made or conclusions are drawn. The two models have depicted a scale of transition from micro (individual members of the society) to macro-level which is a large-scale structure of the society. The paper concludes that either of the two models presented above can solve the micro-macro problem in sociology and social theory in general. Keywords: Aggregation model, coordination model, micro, macro.

A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration

Agglomeration in spray fluidized bed (SFB) is a particle growth process that improves powder properties in the chemical, pharmaceutical, and food industries. In order to analyze the underlying mechanisms behind the generation of SFB agglomerates, modeling of the growth process is essential. Morphology plays an imperative role in understanding product behavior. In the present work, the sequential tunable algorithm developed in previous studies to generate monodisperse SFB agglomerates is improved and extended to polydisperse primary particles. The improved algorithm can completely retain the given input fractal properties (fractal dimension and prefactor) for polydisperse agglomerates (with normally distributed radii of primary particles having a standard deviation of 10% from the mean value). Other morphological properties strongly agreed with the experimental SFB agglomerates. Furthermore, this tunable aggregation model is integrated into the Monte Carlo (MC) simulation. The kinetics of the overall agglomeration at various operating conditions, like binder concentration and inlet fluidized gas temperature, are investigated. The present model accurately predicts the morphological descriptors of SFB agglomerates and the overall kinetics under various operating parameters.

Path Aggregation Model for Attributed Network Embedding with a Smart Random Walk

Network embedding has attracted a surge of attention recently. In this field, how to preserve high-order proximity has long been a difficult task. Graph convolutional network (GCN) and random walk-based approaches can preserve high-order proximity to a certain extent. However, they partially concentrate on the aggregation process and sampling process respectively. Path aggregation methods combine the merits of GCN and random walk, and thus can preserve more high-order information and achieve better performance. However, path aggregation framework has not been applied in attributed network embedding yet. In this paper, we propose a path aggregation model for attributed network embedding, with two main contributions. First, we claim that there always exists implicit edge weight in networks, and design a tweaked random walk algorithm to sample paths accordingly. Second, we propose a path aggregation framework dealing with both nodes and attributes. Extensive experimental results show that our proposal outperforms the cutting-edge baselines on downstream tasks, such as node clustering, node classification, and link prediction.

Discovering Dominant Users’ Opinions in Reddit

Reddit is a social news aggregation and discussion website. Users submit content to the site such as links to news, which are then voted up or down by other members who in turn, can comment on others’ posts to continue the conversation. In this work, we are interested in modeling how users interact with each other in Reddit debates, to discover the most dominant opinions in a debate. To this end, we introduce a user-based model for analysis of Reddit debates. In this model, comments by users are grouped per user, describing their opinion in relation to the root comment of the debate, and users are represented with a single node in a weighted graph, where node’s weights represent relevance of user’s opinions and edges represent agreement or disagreement relationships between users throughout the debate. In this model, agreement or disagreement between the opinions of two users is defined by aggregating the set of single interactions that have occurred between them during the debate. In this work we present a skeptical aggregation model for this task. For measuring the relevance of user’s opinions, we consider two models: one based on the score of all the user’s comments and other based on the user’s karma, as computed by the Reddit platform. We characterize the set of most dominant opinions with an argumentative-based model, using the information of disagreement between opinions and relevance of opinions.

Preference–Approval Structures in Group Decision Making: Axiomatic Distance and Aggregation

Preference–approval structure combines the preference information of both ranking and approval, which extends the ordinal preference model by incorporating two categories of choice alternatives, that is, acceptable (good) and unacceptable (bad), in the preference modeling process. In this study, we present some axioms that imply the existence of a unique distance function of preference–approval structures. Based on theoretical analysis and simulation experiments, we further study a preferences aggregation model in the group decision-making context based on the proposed axiomatic distance function. In this model, the group preference is defined as a preference–approval structure that minimizes the sum of its distances to all preference–approval structures of individuals in the group under consideration. Particularly, we show that the group preference defined by the axiomatic distance–based aggregation model has close relationships with the simple majority rule and Cook and Seiford’s ranking.

The Model for The Classification of The Ripeness Stage of Pomegranate Fruits In Orchards Using

In agriculture, a timely and accurate estimate of ripeness in the orchard improves the post-harvest process. Choosing fruits based on their maturity stages can reduce storage costs and increase market results. In addition, the estimation of the ripeness of the fruit based on the detection of input and output indicators has brought about practical effects in the harvesting process, as well as determining the amount of water needed for irrigation. pepper, the amount of fertilizer for the end of the season appropriate. In this paper, propose a technical solution for a model to detect persimmon green grapefruit fruit at agricultural farms, Vietnam. Aggregation model and transfer learning method are used. The proposed model contains two object detection sub models and the decision model is the pre-processed model, the transfer model and the corresponding aggregation model. Improving the YOLO algorithm is trained with more than one hundred object types, the total proposed processing is 500,000 images, from the COCO image data set used as a preprocessing model. Aggregation model and transfer learning method are also used as an initial step to train the model transferred by the transfer learning technique. Only images are used for transfer model training. Finally, the aggregation model with the techniques used to make decisions selects the best results from the pre-trained model and the transfer model. Using our proposed model, it has improved and reduced the time when analyzing the maximum number of training data sets and training time. The accuracy of model union is 98.20%. The test results of the classifier are proposed through a data set of 10000 images of each layer for sensitivity of 98.2%, specificity 97.2% with accuracy of 96.5% and 0, 98 in training for all grades.