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Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal and vertical system integration, and the industrial internet of things have increased the performance and efficiency of production lines in the manufacturing industry. Over the past years, efforts have been made to propose semantic models to represent the manufacturing domain knowledge, one such model is Reference Generalized Ontological Model (RGOM).1 https://w3id.org/rgom However, its adaptability like other models is not ensured due to the lack of manufacturing data. In this paper, we aim to develop a benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how the I4.0 industry can benefit from KGs and semantic datasets. This work is the result of collaboration with the production line managers, supervisors, and engineers in the football industry to acquire realistic production line data2 https://github.com/MuhammadYahta/ManufacturingProductionLineDataSetGeneration-Football ,.3 https://zenodo.org/record/7779522 Knowledge Graphs (KGs) or Knowledge Graph (KG) have emerged as a significant technology to store the semantics of the domain entities. KGs have been used in a variety of industries, including banking, the automobile industry, oil and gas, pharmaceutical and health care, publishing, media, etc. The data is mapped and populated to the RGOM classes and relationships using an automated solution based on JenaAPI, producing an I4.0 KG. It contains more than 2.5 million axioms and about 1 million instances. This KG enables us to demonstrate the adaptability and usefulness of the RGOM. Our research helps the production line staff to take timely decisions by exploiting the information embedded in the KG. In relation to this, the RGOM adaptability is demonstrated with the help of a use case scenario to discover required information such as current temperature at a particular time, the status of the motor, tools deployed on the machine, etc. [ABSTRACT FROM AUTHOR]

We present the Linked SPARQL Queries (LSQ) dataset, which currently describes 43.95 million executions of 11.56 million unique SPARQL queries extracted from the logs of 27 different endpoints. The LSQ dataset provides RDF descriptions of each such query, which are indexed in a public LSQ endpoint, allowing interested parties to find queries with the characteristics they require. We begin by describing the use cases envisaged for the LSQ dataset, which include applications for research on common features of queries, for building custom benchmarks, and for designing user interfaces. We then discuss how LSQ has been used in practice since the release of four initial SPARQL logs in 2015. We discuss the model and vocabulary that we use to represent these queries in RDF. We then provide a brief overview of the 27 endpoints from which we extracted queries in terms of the domain to which they pertain and the data they contain. We provide statistics on the queries included from each log, including the number of query executions, unique queries, as well as distributions of queries for a variety of selected characteristics. We finally discuss how the LSQ dataset is hosted and how it can be accessed and leveraged by interested parties for their use cases. [ABSTRACT FROM AUTHOR]

This study (among 256 employees and 97 immediate supervisors) examines whether supervisors' learning goal orientation and performance goal orientation are related to employees' mindfulness and whether, in turn, mindfulness is related to employees' creativity and organisational citizenship behavior (OCB). Whereas learning goals focus on non‐judgmental learning from mistakes, performance goals emphasise impressing others and obtaining positive evaluations. Accordingly, reasoning from the perspective of socio‐cognitive theory, we proposed and found that supervisors' learning goal orientation positively relates to employee mindfulness whereas supervisors' performance goal orientation negatively relates to employee mindfulness. Given the broad cognitive and social attentional focus entailed in mindfulness, we further proposed and found that mindfulness is positively related to employees' creativity and OCB and that mindfulness mediates the relations between supervisors' goal orientations and these performance outcomes. We discuss the implications of our findings in light of (a) understanding and managing organisational factors that relate to mindfulness and (b) the implications of achievement goal orientations in leadership processes. [ABSTRACT FROM AUTHOR]

The N-75 highway has a strategic and socio-economic importance in North Pakistan and is prone to frequent disruption by landslides. For landslide mitigation strategies of this highway, comprehensive landslide inventory and suscepti- bility assessment are rarely available. This study presents the application of Analytical Hierarchy Process (AHP) and Weighted Overlay Analysis (WOA) models for the assessment of landslide susceptibility zonation map of the area. To perform these models, nine usual causative factors of landslides (slope, aspect, curvature, lithology, normalised differentiation vegetation index, rainfall, and distance from faults, roads, and streams) were taken into account. For the preparation of thematic maps and other data layers, Digital Elevation Models (DEM), existing geological maps, and authorized data were being processed in GIS environment (ArcMap 10.3). The output landslide susceptibility maps of the study area were classified into five (i.e. very low, low, moderate, high, and very high) landslide susceptibility classes. The competencies of the landslide suscepti- bility zonation maps derived from AHP and WOA models were validated using Area under Curve method. The developed susceptibility maps can be adopted for land use planning and landslide mitigation strategies. [ABSTRACT FROM AUTHOR]

Many popular neighbor designs are used in serology, agriculture, and forestry which manifest neighbor effects very much. If every treatment appears as a neighbor with other (v-2) treatments once but emerges twice with only one treatment, such designs are called Quasi Rees neighbor designs (QRNDs) in k size of circular blocks. These designs were used for counterbalancing the neighboring effects for the cases for which minimal neighbor designs cannot be constructed. In this article, various generators are constructed to obtain circular binary NDs, using cyclic shifts. [ABSTRACT FROM AUTHOR]