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This paper proposes the geometry-based polylinal routing method as a solution for motion planning in object sorting exercises in manufacturing processes. This algorithm is based on geometric properties that arise from the interaction among objects within the configuration space. The method proposed in this paper, during its experimental phase, successfully generated smooth routes with a processing time ranging from 62.5-125 ms on a computer equipped with an AMD Ryzen 7 2700X Eight-Core 3.70 GHz processor and 16 GB of RAM. When compared to the RRT algorithm, it exhibits a higher efficiency of 38% to 48%, resulting in a reduction in iterative processes and a shorter response time. Therefore, the proposed method presents a viable solution for addressing motion planning scenarios in object sorting exercises. [ABSTRACT FROM AUTHOR]

There is a growing interest in the field of social sciences in the study of topics related to the so-called gig economy or platform economy and, among them, in the figure of the rider-a term that refers to the home delivery workers employed through digital platforms. In these platforms, the work of the riders is organized from algorithmic management logics, a mediation in which the panoptic quality of their forms of control and governmentality has tended to be emphasized. In contrast to these frameworks, this paper presents a theoretical-methodological approach that emphasizes the co-constitution-in an open agency- of the rider and the application, to ethnographically approach the everyday labor practices of riders. In the interest of exploring how algorithmic mediation functions in the diversity of riders' experiences, we analyze the coordination tasks responsible for producing the shared space-time geography and the flexible control strategies that mediate the application. The text describes how the algorithm generates a kind of scaffolding that defines directive spatiotemporalities in the riders' operations, within which the deliverers extend the landscape of calculability to those elements that the algorithm cannot consider and to those others that the rider must incorporate in order to make his activity compatible with the algorithmic logic. This article presents the initial results of an ethnography carried out in Madrid since September 2021 with riders from several of the main digital home delivery platforms: Glovo, Uber Eats, and Getir . Along with the fieldwork with riders, we interviewed four technologists in urban logistics. The results show that salaried riders have a less intense experience of the directive chronography of the algorithm, showing how autonomy is not necessarily equivalent to the riders' capability to choose. [ABSTRACT FROM AUTHOR]

This paper deals with the reconfigurations of Noelle-Neumann's (2010) original theory and presents an analysis categorization for the contemporary spiral of silence, which involves four mechanisms: 1) accumulation; 2) consonance; 3) ubiquity; and 4) anonymity. In order to avoid excluding the term social and analyzing meanings and technologies separately, the results in this paper were presented from a sociocultural matrix reception study with eight unionized teachers and eight neo-Pentecostal evangelicals residing in the city of Curitiba (PR). Once determining that algorithms are relevant towards defining new cultural patterns of social interaction, we found the need to investigate consolidated theories in the field of communication and journalism in the light of algorithmic mediations. [ABSTRACT FROM AUTHOR]

In this paper three prime numbers are presented as high potentials to be Mersenne numbers and their application in computational primality testing is suggested. These numbers are constructed from a regression algorithm based on Support vector machines (SVM) and using a Gaussian Kernel. Data training is carried out using the Phyton programming language, In the study we address the current data of Mersenne primes and work with the Ova-angular classification group for Mersenne primes 31. [ABSTRACT FROM AUTHOR]

This paper sets forth an algorithm which can recognize any type of the Spanish voseo conjugations, based on a parametric analysis of its morphological typology. The algorithm is built as a sequence of a minimum of two and a maximum of five operations, or steps. Both the linear arrangement of the steps and the structure of the queries are based on an implicit theory of the relative markedness or complexity of each variant and the parametric analysis of the typology. Thus, assessing the speed and efficiency of the algorithm becomes an indirect measure of the underlying analysis. The algorithm also invites to speculate about how the variable data relates to the way it is acquired or learned. [ABSTRACT FROM AUTHOR]

In this paper we intend to substantiate the usefulness of Deep Learning, especially feedforward neuronal networks, in the prediction of business failure. This methodology provides very good results in terms of predictive performance when large sample sizes are available. Therefore, we have developed a business failure prediction model for European companies, based on this algorithm on a sample of 61,624 companies, of which 12,128 were declared bankrupt in 2016. As independent variables were considered ratios, and economic and financial data obtained from the financial statements for the year preceding the date of failure. Deep Learning achieves a predictive performance of 94%, where companies with larger size and lower solvency are more prone to failure. The obtained results have been tested on an independent test sample, different from that used to estimate and train the model. [ABSTRACT FROM AUTHOR]

In this research paper, we examine forms of work organization implemented in Mercado Libre based on interviews with its workers in Argentina. We analyze the historical course of human resources management until the incursion of the pandemic. As part of the findings, we can point out that the evolution of work management and well-being shows ruptures and continuities, where emotions become increasingly relevant in human resources policies. Inspired by positive psychology in the 90s, such policies focused on the resilient individual. This figure has become more relevant since COVID-19, when the management of intimacy and emotions is mediated by the use of technologies supported by big data. [ABSTRACT FROM AUTHOR]

In this paper, a computer vision inspection methodology is presented for the detection and segmentation of areas of wear by electric arc on the surface of electrical contacts using segmentation by instances. Cadmium is a toxic metal frequently used in the manufacture of alloys for electrical contact, so efforts have been made to replace or eliminate this element from the industry. Our work proposes an approach oriented to the automation of the inspection process, which reduces the subjectivity of the repetitive task of inspecting electrical contacts, allowing to compare two different alloys, such as one with Cadmium (Cd) and the other with Copper (Cu)., subjected to the same conditions and determine which of them presents less wear by electric arc. In this way you can know if the new part is better, equal or worse in terms of performance. Initially, an adaptation was made of the source code hosted in a github repository of mask R-CNN of the Matterport corporation, where variables, classes were modified, and directories were created according to our needs. The results obtained show that the masks generated by the algorithm adjust very well to contact wear, despite the irregular shape of the damage. However, the "charred" edge was probably not considered, because in training images it was a feature that few images presented, so segmentation may not be considering this factor. [ABSTRACT FROM AUTHOR]