Search Results (101)

The evolution of tall buildings has been shaped by distinct architectural styles, beginning around 1875 and progressing through various stylistic architectural movements. These changes were driven by advancements in structural engineering and digital design technologies, leading to greater experimentation with form and function. Energy and resource conservation of the late 20th century instigated a noteworthy focus on sustainability. Beyond that, the early 21st century saw a significant shift toward a new breed of tall buildings, a suitable architectural vocabulary for “high-performance” tall buildings, in which sustainability with a focus on energy efficiency is joined with the performance of other active and passive functional systems. This paper presents an overview of high-performance tall buildings by exploring key technologies, materials, innovations, safety, durability, and indoor environmental quality. Strategies that have emerged to address skyscrapers’ environmental and economic challenges are also crucial in such a building. It highlights the importance of optimizing and integrating building systems, improving energy efficiency, minimizing resource consumption, and ensuring long-term occupant health and productivity. Furthermore, this study identifies five key dimensions—structural materials and systems, energy-efficient design, high-performance façades, performance monitoring, and integrating building services systems—demonstrating how these factors contribute to environment-conscious urban development and resilient architectural and engineering design. It is concluded that these buildings are poised to redefine urban environments by leveraging advanced technologies, AI-driven management, IoT interconnectivity, health-focused elements, and climate resilience. Also, tall, high-performance buildings will be increasingly automated to an unknown limit, and AI will play a prominent role in the future. Full article

While the importance of understanding digital transformation from a Human Resources (HR) perspective is widely acknowledged, a comprehensive analysis of the scholarly landscape remains limited. This study examines the scientific production on digital transformation and HR within the Web of Science database, analyzing bibliometric indicators of production, visibility, impact, and collaboration. The results reveal that digital transformation is a recent, relevant, and transdisciplinary construct that significantly influences talent management strategies and necessitates new skill sets within HR functions. This impact requires organizations to proactively develop digital skills, facilitating a regulated and controlled transition during the implementation of digital strategies. Specifically, organizations must focus on fostering employee adaptability and promoting work–life balance in this rapidly changing environment. Building on these findings, future research could benefit from employing mixed-methods approaches, combining quantitative bibliometric analysis with qualitative case studies to explore the contextual factors influencing the complex relationship between digital transformation and HR, such as organizational culture and leadership styles. Full article

In Europe and elsewhere in the world, current ambitious decarbonization targets push towards a gradual decommissioning of all fossil-fuel-based dispatchable electrical generation and, at the same time, foster a gradual increase in the penetration of Renewable Energy Sources (RES). Moreover, considerations tied to decarbonization as well as to the security of supply, following recent geo-political events, call for a gradual replacement of gas appliances with electricity-based ones. As RES generation is characterized by a variable generation pattern and as the electric carrier is characterized by scarce intrinsic flexibility, and since storage capabilities through electrochemical batteries, as well as demand-side flexibility contributions, remain rather limited, it is quite natural to think of other energy carriers as possible service providers for the electricity system. Gas and heat networks and, in the future, hydrogen networks could provide storage services for the electricity system. This could allow increasing the amount of RES penetration to be managed safely by the electric system without incurring blackouts and avoiding non-economically motivated grid reinforcements to prevent the curtailment of RES generation peaks. What is explained above calls for a new approach, both in electricity network dispatch simulations and in grid-planning studies, which extends the simulation domain to other carriers (i.e., gas, heat, hydrogen) so that a global optimal solution is found. This simulation branch, called multi-energy or multi-carrier, has been gaining momentum in recent years. The present paper aims at describing the most important approaches to static ME modeling by comparing the pros and cons of all of them with a holistic approach. The style of this paper is that of a tutorial aimed at providing some guidance and a few bibliographic references to those who are interested in approaching this theme in the next years. Full article

This article presents a case study that examines why a seemingly effective evangelistic strategy failed within the Sien Church in Wenzhou, China. By tracing the implementation of the religious ministry, it argues that the patriarchal leadership style, goal-oriented strategy, and emphasis on public impression management ultimately undermined the church’s original mission. The pursuit of efficiency and an obsession with numbers created new pressures and anxieties among various teams, transforming the Sien Church’s evangelistic plan into a target-driven competition focused on “baptism numbers” and “conversion rates”. Furthermore, fundamentalist teachings, intertwined with the church’s disciplinary structures, collectively fueled this baptism competition. Finally, the article situates the failure of the gospel project within a broader cultural context and local community. Full article

Consumers encounter significant psychological and social barriers that hinder their participation in ecological transitions. Green influencers (or Greenfluencers), through their communication style and perceived credibility, have the potential to increase consumer awareness and engagement with circular practices. Previous research has focused on green influencers’ role in eco-friendly purchases, but their impact on promoting circular economy behaviors is less studied. To address this gap, the present explorative study investigates how green influencers’ communication can shape consumers’ perceptions of the barriers to adopting circular economies. Using a single case study of the Italian Greenfluencer “@Eco.narratrice” (Elisa Nicoli), this paper examines how green content creators’ communication can address psychological and social obstacles that prevent consumers from embracing circular models in the Italian context. Our findings reveal a different impact of Greenfluencer communication effectiveness on consumer perceptions of circular economy adoption barriers. While Greenfluencer communication helps reduce barriers like knowledge gaps and psychological essentialism, it may also heighten concerns about switching costs, inertia, and cynicism. The findings contribute to the growing literature on green influencers, offering new insights into the broader effects of their communication on consumer engagement with circular economies. This research provides insights for marketing managers on leveraging Greenfluencers to promote circular business models. It also provides feedback for Greenfluencers to refine their communication strategies, helping them better address consumer perceptions of CE barriers, particularly in areas where their efforts may fail to achieve the desired impact. Full article

Supervisors’ chronic regulatory foci significantly influence their leadership styles and behaviors, with prevention focus and promotion focus exerting distinct impacts on their actions and outcomes. Drawing on regulatory focus theory, we propose a conceptual model that links supervisor promotion focus and supervisor prevention focus to new employee task performance through the mediating role of supervisor developmental feedback. We conducted a matched questionnaire survey involving 253 supervisor–new employee pairs at two time points. The findings indicated that supervisor promotion focus was positively associated with supervisor developmental feedback, whereas supervisor prevention focus was negatively associated with supervisor developmental feedback. Furthermore, supervisor developmental feedback enhanced new employees’ task performance. This study elucidates the trickle-down effects of supervisor promotion focus and supervisor prevention focus and offers practical implications for organizations on effectively managing supervisors with varying regulatory foci. Full article

Approximately 24% of the global land area consists of mountainous regions, with 10% of the population relying on these areas for their cultivated land. Accurate statistics and monitoring of cultivated land in mountainous regions are crucial for ensuring food security, creating scientific land use policies, and protecting the ecological environment. However, the fragmented nature of cultivated land in these complex terrains challenges the effectiveness of existing extraction methods. To address this issue, this study proposed a cascaded network based on an improved semantic segmentation model (DeepLabV3+), called Cascade DeepLab Net, specifically designed to improve the accuracy in the scenario of fragmented land features. This method aims to accurately extract cultivated land from remote sensing images. This model enhances the accuracy of cultivated land extraction in complex terrains by incorporating the Style-based Recalibration Module (SRM), Spatial Attention Module (SAM), and Refinement Module (RM). The experimental results using high-resolution satellite images of mountainous areas in southern China show that the improved model achieved an overall accuracy (OA) of 92.33% and an Intersection over Union (IoU) of 82.51%, marking a significant improvement over models such as U-shaped Network (UNet), Pyramid Scene Parsing Network (PSPNet), and DeepLabV3+. This method enhances the efficiency and accuracy of monitoring cultivated land in mountainous areas and offers a scientific basis for policy formulation and resource management, aiding in ecological protection and sustainable development. Additionally, this study presents new ideas and methods for future applications of cultivated land monitoring in other complex terrain regions. Full article

Despite the growing importance of firm-generated content (FGC) on social platforms, the effect of its relevance to products on consumer brand attitudes remains underexplored. As digital platforms increasingly influence consumer decision-making, this study investigates how the relevance of FGC to products affects consumer brand attitudes. Through four experimental studies, we examine the mediating role of brand authenticity and the moderating effects of brand type (time-honored vs. new) and consumer thinking style (analytic vs. holistic). The results indicate that the relevance of FGC to products positively affects consumer brand attitudes, with brand authenticity acting as a mediating factor. Furthermore, this effect is stronger for time-honored brands and analytic-thinking consumers. These findings contribute to digital marketing literature by introducing a novel perspective on FGC and product relevance, an aspect largely overlooked in previous research. Our study also provides valuable insights for brand managers on how to leverage FGC to enhance consumer–brand interactions, particularly by aligning FGC with both brand type and consumer thinking style. Full article

Numerous other scholars have, from an organizational perspective, underscored that leadership styles, such as transformational and transactional leadership, are pivotal antecedents to the safety behaviors of construction workers. However, there is a lack of research exploring the relationship between leadership styles and the safety behaviors of this specific group of new-generation construction workers at the individual level. Relying upon social exchange theory, social cognitive theory, and contingency theory, this research explores the impact of supportive leadership—characterized by care and support—on the safety behaviors (safety compliance and safety participation) of new-generation construction workers. A comprehensive approach combining questionnaire surveys, confirmatory factor analysis, correlation tests, and linear regression was adopted. The results demonstrate that supportive leadership has a significant positive influence on the safety behaviors of new-generation construction workers. Moreover, safety self-efficacy partially mediates this relationship. Additionally, a positive team safety climate enhances the effect of supportive leadership on safety participation. This study provides a theoretical foundation for optimizing leadership styles to improve safety behaviors among new-generation construction workers and offers new insights into the nuanced application of leadership styles in construction safety management. Full article

Ethical management is key to ensuring organizational sustainability, through resources such as autonomy or self-efficacy. However, economic and social uncertainty occasionally leads to adaptive responses that prioritize profit as the primary interest, blurring the integrating role of ethical leadership. There are a number of studies that support this reality in a virtual work environment. This sector-specific and cross-sectional research explores how ethical leadership influences self-efficacy among teleworkers, through active commitment to job autonomy, and how an egoistic climate hinders this influence. The analysis is quantitative and correlational, and the sample includes 448 teleworkers. A model of conditional indirect effects, including both a mediation process and a moderation process, is used. The results support that ethical leadership enhances followers’ self-efficacy through a redistribution of responsibilities, which increases the perception of autonomy. However, when ethical leadership coincides with a climate that has opposing interests, such as an egoistic climate, ethical leadership is unable to counteract it, and its effect on self-efficacy gradually diminishes. The benefits of this management style are widely known, but it is crucial to understand under what circumstances it loses efficacy. This research presents a new theoretical model that contributes to the existing literature on ethical leadership. Lastly, organizations that embrace ethical leadership can avoid the emergence of ethical climates disconnected from collective benefit, such as those characterized by selfishness, which hinder prosocial motivation. In this context, ethical leadership fosters the development of high-quality interpersonal relationships with followers, which are considered essential for creating an environment conducive to group learning. Consequently, change management in organizations necessitates the adoption of an ethical system that enhances self-efficacy through moral principles, rather than relying solely on individualistic aspects. Full article

Data analysis can unearth important insights like patterns, trends, and deductions. In education, it can be utilized to tailor teaching methods to suit student traits or devise new activities to foster different skills or reinforce existing ones, for example. Understanding the particular context and past experiences can aid in this endeavor. Surveys and questionnaires yield a wealth of data. Yet, the sheer volume of questions can lead to challenges in data management for teachers and a decline in student interest due to the time-consuming nature of fulfilling these tasks. This work presents a methodology designed to decrease the number of questions in questionnaires. This method can be applied to general questionnaires that consist of closed-ended questions with a set number of response choices, where each question can have a varying number of options compared to the other questions in the form. This methodology has been adapted into a newly developed software tool for examining learning styles based on a specific learning styles questionnaire: the Honey–Alonso Learning Styles Questionnaire (CHAEA). This software is available to the public and integrated with Moodle, arguably the most extensively used learning management system globally. To evaluate the effectiveness of the proposed method, it has been used across various subjects in Computer Sciences Engineering degrees over different academic years. The outcomes from this case study validate the appropriateness of the technique. Consequently, these findings could establish patterns that could assist in devising more suitable learning methodologies for students. Full article

Image-to-image translation methods have advanced from focusing on image-level info to incorporating pixel-level and instance-level details. However, with feature-level constraint, deviation occurs when the network overemphasizes convolutional features, neglecting traditional image feature extraction. To address this, we proposed the multimodal image translation algorithm MASSE based on a Singular Squeeze-and-Excitation Network, combining GANs and SENet. It utilizes SVD features to assist the SENet in managing the scaling degree. The SENet employs SVD to extract features and enhance the Excitation operation to obtain new channel attention weights and form attention feature maps. Then, image content features are refined by combining convolutional and attention feature maps, and style features are obtained by the style generator. Finally, content and style features are combined to generate new style images. Ablation experiments showed the optimal SVD parameter is 128, producing the best translation results. According to FID, MASSE outperforms current methods in generating diverse images. Full article

With the increasing use of lithium-ion (Li-ion) batteries in electric vehicles (EVs), accurately measuring the state of charge (SoC) has become crucial for ensuring battery reliability, performance, and safety. In addition, EVs operate in different environmental conditions with different driving styles, which also cause inaccurate SoC estimation resulting in reduced reliability and performance of battery management systems (BMSs). To address this issue, this work proposes a new hybrid method that integrates a gated recurrent unit (GRU), temporal convolution network (TCN), and attention mechanism. The TCN and GRU capture both long-term and short-term dependencies and the attention mechanism focuses on important features within input sequences, improving model efficiency. With inputs of voltage, current, and temperature, along with their moving average, the hybrid GRU-TCN-Attention (GTA) model is trained and tested in a range of operating cycles and temperatures. Performance metrics, including average RMSE (root mean squared error), MAE (mean absolute error), MaxE (maximum error), and $R^2$ score indicates the model is performing well, with average values of 0.512%, 0.354%, 1.98%, and 99.94%, respectively. The proposed model performs well under both high and low noise conditions, with an RMSE of less than 2.18%. The proposed hybrid approach is consistently found to be superior when compared against traditional baseline models. This work offers a potential method for accurate SoC estimation in Li-ion batteries, which has an important impact on clean energy integration and battery management systems in EVs. Full article

The intensifying urbanization has led to the increasing risk of urban landscape character fading away. How to inherit and control the “genes” of urban landscape features is a crucial issue that needs urgent resolution. To address this issue, this study draws on the theory of biological genes, employing methods such as historical information translation, deconstruction and extraction of landscape features, and genetic analysis to establish a research paradigm of “holistic composition interpretation—dynamic evolution interpretation” for historic urban landscape genes. The study applies this paradigm in practice using Harbin, China, as an example. The results indicate that: (1) The historic urban landscape genes are comprised of explicit landscape pattern genes; street and alley texture genes; architectural appearance genes; and implicit human settlement culture genes, industrial culture genes, historical culture genes, and spiritual culture genes. (2) The evolutionary traits of Harbin’s historic urban landscape genes manifest in several ways: the evolution of its landscape pattern genes, featuring “three-tiered terraces, balanced spatial arrangement, surrounded by mountains and rivers, and fan-shaped radial layout”; the evolution of its street and alley texture genes, exhibited through “a blend of Chinese and Western styles, sprawling and interconnected, unit linkage, and multi-dimensional intersections”; the evolution of its architectural style genes, marked by “diverse styles, a harmonious fusion of Chinese and Western elements, grouped interdependence, and adaptation to the times”; the evolution of its human settlement culture genes, highlighted by “a secluded lifestyle, diverse customs, and the coexistence of Chinese and Western cultures”; the evolution of its historical culture genes, distinguished by “unity in diversity and multicultural differentiation”; and the evolution of its spiritual culture genes, characterized by “pluralistic beliefs and inclusiveness”. (3) The essential difference between historic urban landscape genes and other genes lies in their adaptive evolution in response to environmental changes over generations and their ability to reveal the influence mechanisms among genes, between genes and landscape features, and between genes and the environment. The distinction between historic urban landscape genes and other types of genes lies in the adaptive changes that historic urban landscape gene types undergo in response to changes in time and environment. The historic urban landscape gene system itself undergoes adaptive evolution with the urban environment of various historical periods, leading to the emergence and differentiation of new levels. This study can provide new insights into urban heritage conservation, urban landscape management, and rational allocation of urban land use. Full article

This publication presents a new method by which control methods based on reinforcement learning can be combined with classical robust control methods. The combination results in a robust management system that meets high-quality criteria. The described method is presented through the control of an autonomous vehicle. By choosing the reward function chosen during reinforcement learning, various driving styles can be realized, e.g., lap time minimization, track tracking, and travel comfort. The neural network was trained using the Proximal Policy Optimization algorithm, and the robust control is based on ${\mathcal{H}}_{\infty }$. The two controllers are combined using a supervisor structure, in which a quadratic optimization task is implemented. The result of the method is a control structure that realizes the longitudinal and lateral control of the vehicle by specifying the reference speed and the steering angle. The effectiveness of the algorithm is demonstrated through simulations. Full article