Search Results (5,685)

With the growing use of AI, it is important to know target audiences’ perceptions of its use. A convenience sample of students were invited to take an online survey in which they were randomly assigned to Group 1 (evaluated a student-generated blog; n = 456) or Group 2 (evaluated an AI-generated blog; n = 492). The results of independent t-tests and chi-squared tests indicated no group differences in ratings of ease of recipe preparation, time to prepare the recipe, utilization of common ingredients, and frequency of intended use of the blog. The student-generated blog was rated higher on budget friendliness (p = 0.025). A total of 42% indicated they would be less willing to use a blog if they knew it was AI-generated, while 43% indicated that it would make no difference and 4.4% indicated being more likely to view the AI-generated blog. Two researchers used a thematic analysis approach to evaluate participants’ free responses regarding the likelihood of using a recipe blog that was AI-generated. Participant perceptions of an AI-generated blog ranged from very positive to very negative. Some themes highlighted the potential benefits of AI or a more neutral stance indicating that “a recipe is a recipe”. The majority of themes highlighted the benefits of content that was created, verified, or tested by humans, or espoused a human touch. Students should be trained to cater to consumer preferences, and to add value in a world that includes AI-generated content. Full article

Objectives: The present study analyzes the effects of a high-intensity interval training (HIIT) program based on the Tabata method on physiological and psychological variables in contemporary dancers (n = 10) and sedentary individuals (n = 8), who performed a 10-week protocol, with sessions of self-loading exercises structured in intervals of 20 s of effort and 10 s of rest three times a week. Methods: Parameters of body composition, muscle strength, aerobic and anaerobic capacity, heart rate variability, as well as perceptions of health, anxiety, stress, sleep quality, and levels of physical activity and sedentary lifestyle were evaluated. Results: The results showed that no significant changes occurred in most body composition variables, except for visceral fat, where group differences were observed (F = 5.66, p = 0.030, η²ₚ = 0.261). In the indicators of strength and power, the dancers improved the height and relative power of the jump (F = 5.996, p = 0.026, η²ₚ = 0.273), while the sedentary ones increased the strength of the handgrip (p = 0.023). In terms of functional performance, both groups significantly increased anaerobic endurance (F = 10.374, p = 0.005, η²ₚ = 0.393), although no changes were recorded in maximal oxygen consumption or heart rate variability (p > 0.05). On a psychological level, improvements in healthy lifestyle habits and a decrease in the trait anxiety variable were evidenced in dancers (p = 0.023), while in sedentary participants no relevant effects were found. Conclusions: In conclusion, the Tabata protocol may represent an efficient and complementary strategy to enhance strength, anaerobic power, and psychological well-being, particularly among dancers. The observed improvements suggest potential benefits related to movement quality, injury prevention, and general physical conditioning. Full article

The application of orchard inspection robots has become increasingly widespread. How-ever, achieving autonomous navigation in unstructured environments continues to pre-sent significant challenges. This study investigates the Simultaneous Localization and Mapping (SLAM) navigation system of an orchard inspection robot and evaluates its performance using Light Detection and Ranging (LiDAR) technology. A mobile robot that integrates tightly coupled multi-sensors is developed and implemented. The integration of LiDAR and Inertial Measurement Units (IMUs) enables the perception of environmental information. Moreover, the robot’s kinematic model is established, and coordinate transformations are performed based on the Unified Robotics Description Format (URDF). The URDF facilitates the visualization of robot features within the Robot Operating System (ROS). ROS navigation nodes are configured for path planning, where an improved A* algorithm, combined with the Dynamic Window Approach (DWA), is introduced to achieve efficient global and local path planning. The comparison of the simulation results with classical algorithms demonstrated the implemented algorithm exhibits superior search efficiency and smoothness. The robot’s navigation performance is rigorously tested, focusing on navigation accuracy and obstacle avoidance capability. Results demonstrated that, during temporary stops at waypoints, the robot exhibits an average lateral deviation of 0.163 m and a longitudinal deviation of 0.282 m from the target point. The average braking time and startup time of the robot at the four waypoints are 0.46 s and 0.64 s, respectively. In obstacle avoidance tests, optimal performance is observed with an expansion radius of 0.4 m across various obstacle sizes. The proposed combined method achieves efficient and stable global and local path planning, serving as a reference for future applications of mobile inspection robots in autonomous navigation. Full article

Autonomous navigation in urban environments demands robust real-time detection of drivable surfaces despite high-throughput LIDAR data. While majority of current approaches often rely on camera-based or multi-sensor fusion systems, this paper introduces an enhancement of our previous LIDAR-centric solution integrated within the Robot Operating System 2 (ROS 2) framework to address computational efficiency and precision challenges. We propose a parallelized algorithm suite for LIDAR-based road and sidewalk detection, achieving processing rates exceeding 20 Hz. Validation on the KITTI benchmark and own datasets demonstrates improved accuracy in complex urban scenarios compared to traditional ground-filtering techniques. To foster reproducibility, the ROS 2-compliant implementation, datasets, and evaluation scripts are publicly released. This work underscores the potential of LIDAR sensors coupled with modern robotic frameworks to enhance perception pipelines in autonomous systems. Full article

With the increasing deployment of unmanned systems in maritime patrol, coastal monitoring, and environmental mapping, achieving effective UAV-USV collaboration in dynamic environments remains challenging. This paper proposes an edge-intelligence-driven collaborative control framework that integrates unified data modeling, multi-objective task scheduling, lightweight fault-tolerant middleware, and multi-sensor fusion. A Weighted Kalman Filter combines UAV imaging and USV sonar data to enhance perception accuracy, while NSGA-II optimizes task allocation considering completion time, energy consumption, and sensing reliability. The framework was validated through representative maritime scenarios, including patrol and coastal sediment mapping, on a virtual simulation platform. Results show improved task efficiency, energy utilization, communication latency, and robustness compared with single-platform and centralized scheduling approaches. The proposed method provides a balanced optimization of execution efficiency, energy consumption, data accuracy, and resilience, offering a reliable solution for large-scale maritime applications. Full article

With the digital transformation of the construction industry toward intelligent construction, advanced digital technologies—including Artificial Intelligence (AI), Digital Twins (DTs), and Internet of Things (IoT)—increasingly support Human–Robot Collaboration (HRC), offering productivity gains while introducing new safety risks. This study presents a systematic review of digital technology applications and risk management practices in HRC scenarios within intelligent construction environments. Following the PRISMA protocol, this study retrieved 7640 publications from the Web of Science database. After screening, 70 high-quality studies were selected for in-depth analysis. This review identifies four core digital technologies central to current HRC research: multi-modal acquisition technology, artificial intelligence learning technology (AI learning technology), Digital Twins (DTs), and Augmented Reality (AR). Based on the findings, this study constructed a systematic framework for digital technology in HRC, consisting of data acquisition and perception, data transmission and storage, intelligent analysis and decision support, human–machine interaction and collaboration, and intelligent equipment and automation. The study highlights core challenges across risk management stages, including difficulties in multi-modal fusion (risk identification), lack of quantitative systems (risk assessment), real-time performance issues (risk response), and weak feedback loops in risk monitoring and continuous improvement. Moreover, future research directions are proposed, including trust in HRC, privacy and ethics, and closed-loop optimization. This research provides theoretical insights and practical recommendations for advancing digital safety systems and supporting the safe digital transformation of the construction industry. These research findings hold significant important implications for advancing the digital transformation of the construction industry and enabling efficient risk management. Full article

This paper assesses the correlates of perceived well-being among young women aged 18 to 30 in five Balkan cities: Athens, Greece; Plovdiv, Bulgaria; Bucharest, Romania; Nis, Serbia; and Shtip, North Macedonia, by integrating urban, travel behavioural, and socio-economic features. A cross-sectional survey was employed using standard questionnaires including the Warwick–Edinburgh Mental Well-being Scale (WEMWBS), the short version of the International Physical Activity Questionnaire (IPAQ), and the adapted ALPHA environmental questionnaire. To answer research questions, linear regression models were developed to analyse predictors of well-being at both regional and national levels. Results show that neighbourhood and mobility features play a significant role in shaping mental well-being. Access to walkable sidewalks, green spaces, mixed land-use structure, and attractive local facilities (e.g., shops, recreational centres in the neighbourhood) were consistently associated with higher levels of well-being. Conversely, perceived insecurity, especially at night or regarding bicycle theft, significantly reduced well-being. Physical activity levels, particularly days of walking and vigorous activity, showed strong positive associations, underscoring the role of active lifestyles in promoting mental health. Socio-economic variables, including financial status, relationship status, and work status, were also found to be linked to perceived well-being. Cycling-related variables may affect Greek well-being up to 16.5 times. Perception of crime during the night may negatively affect both Bulgarian and Serbian well-being (up to 10 times), while Romanian well-being is mostly affected by the existence of shopping facilities. Finally, the most impactful factors for well-being in North Macedonia refer to cycling safety and scooter accessibility. Full article

Background/Objectives: Given the high prevalence of mental health problems in the workplace, fostering disclosure and reducing sickness absence are critical for ensuring timely support and sustaining employees’ work ability. Drawing on the health-oriented riented leadership (HoL) model, this paper examines the associations between staff care, disclosure, and sickness absence, and addresses the underexplored question of whether staff care continues to show beneficial relationships when employees experience acute health deterioration. To account for differing perspectives, we included samples with employees and with leaders. Methods: We conducted three distinct cross-sectional studies with (1) predominantly healthy employees (N₁ = 148), (2) employees with severe mental health issues or a diagnosis (N₂ = 338), and (3) leaders (N₃ = 91). Results: Staff care is positively related to disclosure across all studies. In study 1, this relationship was unexpectedly stronger for low than for high health deterioration, though still significant for high deterioration. In studies 2 and 3, the interaction was non-significant. However, a perceptual gap emerged: simple slopes showed that leaders with low staff care still expected disclosure from employees with high health deterioration (study 3), whereas employees reported higher concealment intentions (study 1). Staff care was negatively related to sickness absence only in study 2, with this relationship strengthened under high health deterioration. Conclusions: Staff care seems particularly relevant for supporting disclosure during early health declines and for mitigating sickness absence during acute deterioration among those already affected. Divergent leader–employee perceptions may hinder timely support. We provide practical recommendations for organizations. Full article

Background/Objectives: Time is a valuable and limited source that must be managed to participate in life domains efficiently. Time perception is an essential skill for managing the time, based on executive functions. This study examined the effects of animation therapy on time perception and daily routines in 7–10-year-old children through executive functions. Methods: Sixty-three typically developing children were randomly assigned to intervention and control groups. The intervention group received 10 weeks of animation therapy, while the control group followed daily routines. Time perception was assessed using Duration Discrimination and Reproduction tasks, and daily routines with the Executive Functions and Occupational Routines Scale (EFORTS). Results: The intervention group showed significant improvements in Duration Discrimination (500 ms: p = 0.02; 800 ms: p = 0.01), Duration Reproduction (800 ms: p = 0.05), and EFORTS subscales (p = 0.00), except Morning and Evening Routines (p > 0.05). Medium to large effect sizes were observed for time perception (r = −0.34 to −0.61) and small to large for EFORTS (r = −0.28 to −0.75). The control group showed no to small effects in time perception (r = −0.02 to −0.14) and no to medium effects in EFORTS (r = −0.07 to −0.45). Conclusions: Animation therapy may enhance children’s synchronization of time perception with physical time, benefiting social, leisure, and play routines through executive functions. Thus, it could be a valuable addition to occupational therapy interventions. Full article

The study presented in this article explores the changing significance of the river and its impact on shaping the city’s image, using the example of the relationship between the Oder River and the city of Szczecin. The main objective was to examine how the Oder influences Szczecin’s image in the context of sustainable development. The research was based on a historical-interpretative method, employing the analysis of over three thousand postcards depicting the riverside areas of Szczecin from a period of approximately 170 years (1850–2024). The quantitative analysis of postcards was supplemented with an analysis of semantic networks. This approach made it possible to verify how representations of the river on historical postcards reflect the evolution of Szczecin’s urban identity and its connection with the idea of sustainability. The study identified the dominant meanings of the river in different historical periods, as well as characteristic views and distinctive landmarks. This allowed for an assessment of how the Oder was perceived and how these perceptions shaped the city’s image. The results indicate that Szczecin’s image has evolved over time, yet it has always remained rooted in its relationship with the river, dependent on how the Oder was perceived and valued. Today, the river represents not only an essential element of the city’s landscape and cultural identity but also a key component of its contemporary image as a sustainable city. The study contributes to understanding how riverfront imagery shapes perceptions of urban sustainability. Full article

Background/Objectives: The COVID-19 pandemic has impacted rural communities in unique ways. Those living in rural communities encounter several challenges in managing the effects of COVID-19, and exploring the perceptions of those from rural communities provides valuable information about rural health behaviors. This study aimed to explore the various reactions that individuals in a predominantly rural Midwestern state had regarding the COVID-19 pandemic and its impact on them at two time points (December 2020 and March 2021) during the initial rollout of the COVID-19 vaccination. Methods: Utilizing an inductive thematic approach to analyze data, researchers found several themes reflecting the participants’ reactions to COVID-19. Results: Participants described varying reactions to public health information around COVID-19 and how those reactions, and subsequent behaviors, were impacted by different rural values. The themes that emerged from the data were Vulnerability factors, Experiences of Emotions, Government Response, COVID-19 Guidelines, Politicization of Pandemic, and Hope/Optimism. Conclusions: The findings suggest the importance of a community-responsive approach to implementing public health interventions that align with community values and priorities. Using behaviorally based interventions that acknowledge individual experiences, beliefs, capacity/resources, and cultural norms may be effective in supporting promotive health behaviors in rural communities. Full article

The stages of change have been identified as a valuable framework for understanding the transition toward a healthy lifestyle. It is also important to recognize change through other psychosocial variables, such as motivation and self-efficacy. The objective of this study was to explore weight control over the course of an academic year (nine months) through three behaviors: the stage of change toward weight control (pre-contemplation, contemplation, preparation, action, maintenance), healthy and unhealthy eating behaviors for weight control, and the frequency of physical activity (PA). Furthermore, we wanted to ascertain whether the three distinct types of motivation (autonomous, controlled, and amotivation) and self-efficacy could account for fluctuations in weight control over time. The sample consisted of 303 adolescents (205 female and 98 male) between the ages of 15 and 23 (M = 17.26; SD = 1.65). Chi-square, t-test, and multiple linear stepwise regression analysis were employed. The results indicated that a higher proportion of adolescents were in the precontemplation and action stages at Time 2. Concurrently, an increase in the frequency of moderate-to-vigorous PA and an increase in healthy and unhealthy behaviors were observed during the school period. The present study posits that autonomous motivation, controlled motivation, and self-efficacy can explain healthy eating behaviors for weight control and the frequency of moderate-to-vigorous PA, while only controlled motivation explains unhealthy eating behaviors for weight control. The conclusion of the study points out that healthy behaviors can change over time due to individual regulation of motivation and increased self-perception of efficacy in one’s own abilities to perform a specific action to control weight. Full article

As tangible relics of modern colonial history, Manchurian regime (Manchukuo) architecture of Changchun possesses both historical commemorative value and tourism and cultural functions. Public perception and sentiment regarding this heritage in the contemporary social media context are key dimensions for evaluating the effectiveness of cultural regeneration. Existing research on Manchurian regime architecture has focused primarily on historical research and architectural form analysis, with limited research examining the diverse public interpretations of its cultural value through multi-platform social media data. This study aims to systematically explore the public’s cognitive characteristics, sentimental attitudes, and themes of interest regarding Changchun’s Manchurian regime architecture using online ethnographic data, providing empirical support for optimizing cultural regeneration pathways for Manchurian regime architectural heritage. The study collected data from 1 January 2020 to 20 September 2025, using the keyword “Changchun Manchurian regime architecture”. Using Python crawlers, the study extracted 334 original videos and 18,156 related comments from Douyin, Ctrip, and Dianping. The analysis was conducted using word frequency statistics, SnowNLP sentiment analysis, LDA topic modeling, and multidimensional visualization. The study found that (1) word frequency statistics show that the public has multiple concerns about the historical symbols, geographical positioning, cultural and tourism functions, and national emotions of Manchurian regime architecture; (2) SnowNLP analysis shows that positive comments account for 71%, neutral comments account for 11%, and negative comments account for 18%; (3) the optimal number of topics was determined to be five through perplexity and consistency indicators, namely “historical narrative and imperial power symbols”, “emotional experience and historical reflection”, “visit experience and service facilities”, “site distribution and regional space”, and “explanation and tour evaluation”; (4) the corpus can be divided into five time period stages, namely S1 (2020)–S5 (2024–2025), reflecting the shift in public attention from “space-facilities” to in-depth reflection on “emotion-history”. Full article

This review critically examines advancements in sensing, control, and networking technologies for agricultural robots (AgRobots) and their impact on modern farming. AgRobots—including Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and robotic arms—are increasingly adopted to address labour shortages, sustainability challenges, and rising food demand. This paper reviews sensing technologies such as cameras, LiDAR, and multispectral sensors for navigation, object detection, and environmental perception. Control approaches, from classical PID (Proportional-Integral-Derivative) to advanced nonlinear and learning-based methods, are analysed to ensure precision, adaptability, and stability in dynamic agricultural settings. Networking solutions, including ZigBee, LoRaWAN, 5G, and emerging 6G, are evaluated for enabling real-time communication, multi-robot coordination, and data management. Swarm robotics and hybrid decentralized architectures are highlighted for efficient collective operations. This review is based on the literature published between 2015 and 2025 to identify key trends, challenges, and future directions in AgRobots. While AgRobots promise enhanced productivity, reduced environmental impact, and sustainable practices, barriers such as high costs, complex field conditions, and regulatory limitations remain. This review is expected to provide a foundation for guiding research and development toward innovative, integrated solutions for global food security and sustainable agriculture. Full article

During the development of vehicle control algorithms, effective real-world validation is crucial. Model vehicle platforms provide a cost-effective and accessible method for such testing. The open-source F1Tenth project is a popular choice, but its reliance on lidar sensors limits certain applications. To enable more universal environmental perception, integrating a stereo camera system could be advantageous, although existing software packages do not yet support this functionality. Therefore, our research focuses on developing a modular software architecture for the F1Tenth platform, incorporating real-time stereo vision-based environment perception, robust state representation, and clear actuator interfaces. The system simplifies the integration and testing of control algorithms, while minimizing the simulation-to-reality gap. The framework’s operation is demonstrated through a real-world control problem. Environmental sensing, representation, and the control method combine classical and deep learning techniques to ensure real-time performance and robust operation. Our platform facilitates real-world testing and is suitable for validating research projects. Full article