Abstract
The speed and scale of technological change are raising concerns about the extent to which new technologies will radically transform workplaces. Competition for the best talent is being intensified, and talent management requires new approaches and innovative strategies for developing talent based on corporate culture and its unique properties. By implementing and adopting technology in Human Resources Management (HRM), organizations create a digital employee lifecycle that spans from the initial Hiring Process to encompassing areas such as Performance Management, Learning and Development until the Offboarding, shaping a Talent Management journey. Despite the implementation of technologies being a continuous practice observed in numerous organizations, there are still challenges. The HRM technological market has become massive, and concerns arise about adopting these technologies' costs, practicality, and purpose. Because of that, designing strategies for implementing technologies in HRM, specifically in talent management, is hard to overview. In this context, this document aims to present the necessity and significance in developing a framework that aggregates the implementation process of technologies in talent management supported by Design Science Research (DSR). The holistic perspective of the forthcoming framework consolidates insights into business challenges and their correlation with technology selection, technological capabilities, implementation procedures, as well as anticipated metrics and their impact.

Abstract
Involving people in urban planning offers many benefits, but current methods are failing to get a large number of citizens to participate. People have a high participation barrier when it comes to public participation in urban planning - as it requires a lot of time and initiative, only a small non-diverse group of citizens take part in governmental initiatives. In this paper, a product is developed to make it as easy as possible for citizens to get involved in construction projects in their community at an early stage. As a solution, a public screen is proposed, which offers citizens the opportunity to receive information, view 3D models, vote and comment at the site of the construction project via smartphone - the solution was named Parcitypate. To explain the functions of the product, a prototype was created and tested. In addition, concepts for branding, marketing, ethics, and sustainability are presented.

Abstract
Remote sensing, particularly satellite-based, can play a valuable role in monitoring areas prone to geohazards. The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards. This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data, with a particular emphasis on: (1) detecting mining subsidence, where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar (InSAR) technology. The results showed that the Efficient Channel Attention (ECA) U-Net model performed better than the U-Net (baseline) model in terms of Mean Intersection over Union (MIoU) and Intersection over Union (IoU) indicators; (2) monitoring water conservancy and hydropower engineering. The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets (SBAS) InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side. The dam body also showed obvious deformation with a velocity exceeding 60 mm/a; (3) the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation. The overall outcome of these methods showed that the use of Artificial Intelligence (AI) techniques in combination with InSAR data leads to more efficient analysis and interpretation, resulting in improved accuracy and prompt identification of potential hazards; and (4) finally, this study also presents a method for detecting landslides in mountainous regions, using optical imagery. The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods, this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades.

Abstract

Abstract
The increase in digital teaching and learning methodologies creates the opportunity for new educational approaches, both in terms of pedagogical practice and in the availability of new technological tools. The flipped classroom as an active teaching methodology is one example of blended learning (b-learning), which aims to harmonize and enhance the fusion of face-to-face teaching with online teaching, allowing students to get better use of both face-to-face contact with classmates and professors and digital teaching resources. However, active teaching methodologies allow us to merge educational techniques from different methodological approaches, for example, gamification and team-based learning (TBL), among others. This study aims to demonstrate how to implement a flipped classroom with the possibility of integrating gamification and TBL, indicating possibilities and challenges to overcome, through the comparative study and research carried out with students in higher education. The study was conducted with a group of 88 students from the engineering and technology fields, which showed that students have a very positive perception of active teaching methodologies and their teaching and learning techniques, especially those involving digital. Data collection was performed by a survey submitted to quantitative analysis using the Software SPSS version 28. © 2024 the author(s)

Abstract
This paper is concerned with the semantic segmentation within domain-specific contexts, such as those pertaining to biology, physics, or material science. Under these circumstances, the objects of interest are often irregular and have fine structure, i.e., detail at arbitrarily small scales. Empirically, they are often understood as self-similar processes, a concept grounded in Multifractal Analysis. We find that this multifractal behaviour is carried out through a convolutional neural network (CNN), if we view its channel-wise responses as self-similar measures. A function of the local singularities of each measure we call Singularity Stregth Recalibration (SSR) is set forth to modulate the response at each layer of the CNN. SSR is a lightweight, plug-in module for CNNs. We observe that it improves a baseline U-Net in two biomedical tasks: skin lesion and colonic polyp segmentation, by an average of 1.36% and 1.12% Dice score, respectively. To the best of our knowledge, this is the first time multifractal-analysis is conducted end-to-end for semantic segmentation.