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ispartof: ITALIAN JOURNAL OF ANIMAL SCIENCE vol:22 issue:1 pages:749-753 status: accepted

The digital transformation of companies involves a set of substantial changes in all areas of the organization. This study analyses the influence of digital transformation on talent management processes. In an effort to determine whether companies make different investments in each, we analyse talent management by separating the variables that attract and retain talent. The sample under study is made up of 314 Spanish companies who are currently undergoing the process of digital transformation. Company data were obtained through a questionnaire answered by managers of these organizations. The statistical technique used to test the model assumptions was a structural equation model. The results obtained lead us to accept the model hypotheses. The organizational changes brought about by digital transformation are thus seen to influence talent management and to attract and retain talent.

Remote sensing data is a cheap form of surficial geoscientific data, and in terms of veracity, velocity and volume, can sometimes be considered big data. Its spatial and spectral resolution continues to improve over time, and some modern satellites, such as the Copernicus Programme's Sentinel-2 remote sensing satellites, offer a spatial resolution of 10 m across many of their spectral bands. The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data. The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data, which can be used for numerous downstream activities, particularly where data timeliness, volume and velocity are important. Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry, which currently entirely relies on manually derived data that is primarily guided by scientific reduction. Furthermore, it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis. Currently, no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences. In this paper, we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation. We use gold grade data from a South African tailing storage facility (TSF) and data from both the Landsat-8 and Sentinel remote sensing satellites. We show that various machine learning algorithms can be used given the abundance of training data. Consequently, we are able to produce a high resolution (10 m grid size) gold concentration map of the TSF, which demonstrates the potential of our method to be used to guide extraction planning, online resource exploration, environmental monitoring and resource estimation. Validerad;2023;Nivå 1;2023-04-12 (hanlid);Funder: Department of Scienceand Innovation (DSI) - National Research Foundation (NRF) ThuthukaGrant (121,973); SI-NRF CIMERA; Centre for Advanced Miningand Metallurgy (CAMM), Luleå University of Technology; Sibanye-Stillwater Ltd

In this work, an internet of things (IoT) sensing and monitoring box has been developed. The proposed low-cost system is a portable device for smart buildings to measure vibrations, monitor, and control noise caused by the industrial machines. We will present an instrument and a method to measure the vibration and tilt of a mechanical system (air conditioner). The primary goal is to create a signal acquisition and monitoring system that is both user-friendly and affordable, while also delivering exceptional precision. The key concept is centered around acquiring and processing signals through the Raspberry Pi. We will use for the first time as an application, which does not exist before, a conversion method to control and monitor remotely the noise generated by the machines. Once the noise reaches a high value or the air conditioner is too much tilted, the system sends an alert in the form of an email. We will use the Python language to acquire and process the signal and send the alerts. The proposed approach is straightforward to implement, and the obtained results demonstrate a high level of accuracy that is consistent with the existing literature.

Mountain snowpack is an important resource for water management planning in Utah. Snow water equivalent (SWE) is the amount of water contained in a snowpack. A few organizations predict SWE throughout the United States but struggle making accurate predictions in mountainous regions. Weather stations provide accurate measurements of SWE but have limited spatial coverage that hinders the ability to make accurate estimates statewide. This thesis examines the accuracy of current models and proposes using local weather measurements to improve upon national level predictions. An R statistical software package named rsnodas implements this process while allowing the public access to a variety of temperature and SWE datasets. The package also provides a method for combining predictions from different data sources, which has the advantage of improving the stability of the predictions over time and space. This thesis includes a discussion of the general merits of the map combination approach along with potential avenues for improvement in the future.

In this paper, we present an internet of things (IoT) powered solution that facilitates effortless monitoring and management of vertical farming operations. Our proposed approach employs cost-effective embedded microcontrollers and sensors to keep a tab on crucial parameters like soil moisture, air humidity, and temperature. The data acquired from these sensors can be accessed through a web page that is compatible with all web browsers and smart gadgets such as mobile phones and tablets. Furthermore, the IoT platform offers users the ability to regulate soil moisture and administer ultraviolet light to plants. The system can bring many benefits such as enabling real-time monitoring and control of environmental conditions, reducing energy consumption, improving scalability and flexibility, and contributing to the sustainable and efficient production of food.

As technology continues to advance, governments around the world have implemented health protocols to minimize direct contact between individuals and objects, in response to the ongoing COVID-19 outbreak. To address this need, a touchless parking portal was designed using a microcontroller-based and internet of things (IoT) -based system, with the Arduino UNO microcontroller device serving as the core component. The system employs an ultrasonic sensor HC-SR04 and passive infrared (PIR) to detect vehicles as they arrive at the portal area, in addition to requiring an ESP32-CAM camera, servo motor, light-emitting diode (LED), I2C 16x2 liquid crystal display (LCD), push button, universal serial bus (USB) to transistor-transistor logic (TTL) converter, power supply, and portal bar. The system builder software was developed using Arduino integrated development environment (IDE), Android, and Blynk. The authors conducted thorough testing and analysis of the system, concluding that its overall performance reaches 100%. Nevertheless, despite the extensive experimentation conducted, there remains a possibility that certain factors could still affect the results. Therefore, caution is advised when interpreting the outcomes of this experiment.

Internet of things (IoT) smart technology enables new digital agriculture. Technology has become necessary to address today's challenges, and many sectors are automating their processes with the newest technologies. By maximizing fertiliser use to boost plant efficiency, smart agriculture, which is based on IoT technology, intends to assist producers and farmers in reducing waste while improving output. With IoT-based smart farming, farmers may better manage their animals, develop crops, save costs, and conserve resources. Climate monitoring, drought detection, agriculture and production, pollution distribution, and many more applications rely on the weather forecast. The accuracy of the forecast is determined by prior weather conditions across broad areas and over long periods. Machine learning algorithms can help us to build a model with proper accuracy. As a result, increasing the output on the limited acreage is important. IoT smart farming is a high-tech method that allows people to cultivate crops cleanly and sustainably. In agriculture, it is the use of current information and communication technologies.

Robotic manipulation of cloth is a complex task because of the infinite-dimensional shape-state space of textiles, which makes their state estimation very difficult. In this paper we introduce the dGLI Cloth Coordinates, a finite low-dimensional representation of cloth states that allows us to efficiently distinguish a large variety of different folded states, opening the door to efficient learning methods for cloth manipulation planning and control. Our representation is based on a directional derivative of the Gauss Linking Integral and allows us to represent spatial as well as planar folded configurations in a consistent and unified way. The proposed dGLI Cloth Coordinates are shown to be more accurate in the representation of cloth states and significantly more sensitive to changes in grasping affordances than other classic shape distance methods. Finally, we apply our representation to real images of a cloth, showing that with it we can identify the different states using a distance-based classifier. This work was developed under the project CLOTHILDE which has received funding from the European Research Council (ERC) under the EU-Horizon 2020 research and innovation programme (grant agreement No. 741930). M. Alberich-Carramiñana is also with the Barcelona Graduate School of Mathematics (BGSMath) and the Institut de Matemàtiques de la UPC-BarcelonaTech (IMTech), and she and J. Amorós are partially supported by the Spanish State Research Agency AEI/10.13039/501100011033 grant PID2019-103849GB-I00 and by the AGAUR project 2021 SGR 00603 Geometry of Manifolds and Applications, GEOMVAP. J. Borràs is supported by the Spanish State Research Agency MCIN/ AEI /10.13039/501100011033 grant PID2020-118649RB-I00 (CHLOE-GRAPH project). Peer Reviewed