Search Results (8,468)

Path-planning algorithms for planetary rovers are critical for autonomous robotic exploration, enabling the efficient and safe traversal of complex and dynamic extraterrestrial terrains. Unlike terrestrial mobile robots, planetary rovers must navigate highly unpredictable environments influenced by diverse factors such as terrain variability, obstacles, illumination conditions, and temperature fluctuations, necessitating advanced path-planning strategies to ensure mission success. This review comprehensively synthesizes recent advancements in planetary rover path-planning algorithms. First, we categorize these algorithms from a constraint-oriented perspective, distinguishing between internal rover state constraints and external environmental constraints. Next, we examine rule-based path-planning approaches, including graph search-based methods, potential field methods, sampling-based techniques, and dynamic window approaches, analyzing representative algorithms in each category. Subsequently, we explore bio-inspired path-planning methods, such as evolutionary algorithms, fuzzy computing, and machine learning-based approaches, with a particular emphasis on the latest developments and prospects of machine learning techniques in planetary rover navigation. Finally, we synthesize key insights from existing algorithms and discuss future research directions, highlighting their potential applications in planetary exploration missions. Full article

Background/Objectives: Klebsiella pneumoniae ST307 is emerging as a significant global high-risk antimicrobial-resistant (AMR) clone with a notable capacity to acquire and disseminate resistance genes. However, there is limited research on the pathogenicity, virulence, and adaptation of ST307 strains and on the clinical characteristics of infected patients. Methods: In this study, a carbapenem-resistant K. pneumoniae (CRKP) ST307 strain named U989 was isolated from a urine culture of a hospitalized patient in Volos, Greece, in July 2024. Whole-genome sequencing was performed to identify resistance genes to β-lactams bla_KPC-2, bla_CTX-M-15, bla_TEM-1B, bla_OXA-1, bla_OXA-10, bla_SHV-106, and bla_VEB-1 and resistance genes to other antibiotics. Results: A genomic analysis also revealed the presence of virulence factors such as iutA, clpK1, fyuA, fimH, mrkA, Irp2, and TraT and an IncFiB(pQil)/IncFII(K) replicon, which harbors the bla_KPC-2 gene. Additionally, the transposable element Tn4401 was identified as a key vehicle for the mobilization of the bla_KPC-2 resistance gene. Finally, this is the first report of a high-risk CRKP ST307 clone expressing KPC-2, SHV-106, CTX-M-15, and VEB-1 bla genes in Greece. Conclusions: The coexistence of these resistance genes in addition to aminoglycoside, quinolone, and other resistance genes results in difficult-to-treat infections caused by respective carrier strains, often requiring the use of last-resort antibiotics and contributing to the global challenge of antimicrobial resistance. Full article

Complexity of non-orthogonal multiple access (NOMA) digital signal processing schemes is particularly relevant in mobile environments because of the varying channel conditions of every single user. In contrast to legacy modulation and coding schemes (MCSs), NOMA MCSs typically have irregular symbol constellations with asymmetric symbol decision regions affecting synchronization at the receiver. Research papers investigating signal processing in this emerging field usually lack sufficient details for facilitating software-defined radio (SDR) implementation. This work presents a new symbolic framework approach for simulating signal processing functions in SDR transmit–receive paths in a dynamic NOMA downlink use case. The proposed framework facilitates simple and intuitive implementation and testing of NOMA schemes and can be easily expanded and implemented on commercially available SDR hardware. We explicitly address several important design and measurement parameters and their relationship to different tasks, including variable constellation processing, carrier and symbol synchronization, and pulse shaping, focusing on quadrature amplitude modulation (QAM). The advantages of the proposed approach include intuitive symbolic modeling in a dynamic framework for NOMA signals; efficient, more accurate, and less time-consuming design flow; and generation of synthetic training data for machine-learning models that could be used for system optimization in real-world use cases. Full article

A thorough understanding of forest resources and development trends is based on quick and accurate forest inventories. Because of its flexibility and localized independence, mobile laser scanning (MLS) based on simultaneous localization and mapping (SLAM) is the best option for forest inventories. The gap in the review studies in this field is filled by this study, which offers the first comprehensive review of SLAM-based MLS in forest inventory. This synthesis includes methods, research progress, challenges, and future perspectives of SLAM-based MLS in forest inventory. The precision and efficiency of SLAM-based MLS in forest inventories have benefited from improvements in data collection techniques and the ongoing development of algorithms, especially the application of deep learning. Based on evaluating the research progress of SLAM-based MLS in forest inventory, this paper provides new insights into the development of automation in this field. The main challenges of the current research are complex forest environments, localized bias, and limitations of the algorithms. To achieve accurate, real-time, and applicable forest inventories, researchers should develop SLAM technology dedicated to forest environments in the future so as to perform path planning, localization, autonomous navigation, obstacle avoidance, and point cloud recognition. In addition, researchers should develop algorithms specialized for different forest environments and improve the information processing capability of the algorithms to generate forest maps capable of extracting tree attributes automatically and in real time. Full article

Parking availability prediction is a critical component of intelligent transportation systems, aiming to reduce congestion and improve urban mobility. While traditional deep learning models such as Long Short-Term Memory (LSTM) networks have been widely applied, they lack mechanisms to quantify uncertainty, limiting their robustness in real-world deployments. This paper proposes a Bayesian Neural Network (BNN)-based framework for parking occupancy prediction that explicitly models both epistemic and aleatoric uncertainty. Although BNNs have shown promise in other domains, they remain underutilised in parking prediction—likely due to the computational complexity and the absence of real-time context integration in earlier approaches. Our approach leverages contextual features, including temporal and environmental factors, to enhance uncertainty-aware predictions. The framework is evaluated under varying data conditions, including data scarcity (90%, 50%, and 10% of training data) and synthetic noise injection to simulate aleatoric uncertainty. Results demonstrate that BNNs outperform other methods, achieving an average accuracy improvement of 27.4% in baseline conditions, with consistent gains under limited and noisy data. Applying uncertainty thresholds at 20% and 30% further improves reliability by enabling selective, confidence-based decision making. This research shows that modelling both types of uncertainty leads to significantly improved predictive performance in intelligent transportation systems and highlights the potential of uncertainty-aware approaches as a foundation for future work on integrating BNNs with hybrid neuro-symbolic reasoning to enhance decision making under uncertainty. Full article

Aiming at the operation optimization and evaluation problems of a park-level integrated energy system with mobile robots, the current research status and main problems are reviewed from three aspects: classification of operation optimization objectives, sorting of evaluation methods, establishment of evaluation index system, and selection of evaluation methods. In terms of target classification, a clear taxonomy can be established by categorizing objectives into quantitative and qualitative indicators. From the perspectives of the economic, technical, environmental, and social dimensions, each indicator can be organized into three levels for systematic analysis and discussion. In terms of evaluation methods, the common evaluation methods of the park-level integrated energy system in the past ten years are summarized and organized. Then, the common secondary indicators are analyzed, the principle of the establishment of the evaluation index system is summarized, and suggestions are given for the selection of combined evaluation methods by discussing the common evaluation methods. Finally, the content is summarized and the research work on the operation optimization objectives and evaluation methods of the park-level integrated energy system is prospected. Full article

Intelligent transport systems are essential for urban residents in large cities, facilitating not only vehicular mobility but also the movement of residents. Urban mobility is a significant concern, particularly in the context of the Internet of Things, where vehicles evolve into intelligent nodes within sensor networks. This convergence of the mobile Internet and the Internet of Vehicles (IoV) redefines urban mobility. In the context of smart cities, it examines the evolving IoV and communication models, unveiling both current and emerging trends. This research paper offers insights into global market trends and conducts bibliographic data analysis to illuminate the present and future potential of the IoV. It highlights IoV applications, the layered architecture, and connected and autonomous vehicle levels (Level 0 to Level 5). The communication model is explained, along with addressing research challenges and future directions. The conclusion summarizes the key findings and emphasizes the main points addressed in the study. Full article

Urban environments and climate-related challenges impact older adults’ health and well-being. To address these challenges, climate adaptation strategies and urban design guidelines should be tailored to older adults’ needs. Ethnographic studies can help identify these needs by involving them directly in the research process. This study uses ethnographic research to explore older adults’ perceptions and behaviours regarding climate change risks and impacts, health, and mobility challenges in a vulnerable urban area—São Roque da Lameira, Porto, Portugal. It studies the applicability and complementarity of four participatory methods that can inform urban design: (I) semi-structured interviews, (II) ‘go-along’ interviews, (III) user observations, and (IV) emotional mapping. The qualitative data collected were analysed through thematic and spatial analysis. Common themes emerged between the four methods, including concerns about accessibility, safety, and comfort, such as uneven pavements, lack of seating, and poor infrastructure for people with reduced mobility. Participants recommended improvements, such as more green spaces and better pedestrian infrastructure quality. Notably, each method uncovered distinct dimensions, highlighting the added value of a multi-method approach. This study demonstrates that combining participatory methods offers deeper, context-specific insights to inform age-friendly and climate-resilient urban design. Future research should take climate-focused methods and a multidisciplinary approach into consideration. Full article

Trust management provides an alternative solution for securing open, dynamic, and distributed multi-agent systems, where conventional cryptographic methods prove to be impractical. However, existing trust models face challenges such as agent mobility, which causes agents to lose accumulated trust when moving across networks; changing behaviors, where previously reliable agents may degrade over time; and the cold start problem, which hinders the evaluation of newly introduced agents due to a lack of prior data. To address these issues, we introduced a biologically inspired trust model in which trustees assess their own capabilities and store trust data locally. This design improves mobility support, reduces communication overhead, resists disinformation, and preserves privacy. Despite these advantages, prior evaluations revealed the limitations of our model in adapting to provider population changes and continuous performance fluctuations. This study proposes a novel algorithm, incorporating a self-classification mechanism for providers to detect performance drops that are potentially harmful for service consumers. The simulation results demonstrate that the new algorithm outperforms its original version and FIRE, a well-known trust and reputation model, particularly in handling dynamic trustee behavior. While FIRE remains competitive under extreme environmental changes, the proposed algorithm demonstrates greater adaptability across various conditions. In contrast to existing trust modeling research, this study conducts a comprehensive evaluation of our model using widely recognized trust model criteria, assessing its resilience against common trust-related attacks while identifying strengths, weaknesses, and potential countermeasures. Finally, several key directions for future research are proposed. Full article

Wealth and power have been recognized as being unequally concentrated within specific families. While research on social mobility has been limited to defined outcomes (e.g., occupation, income, and education) in specific contexts, the rise of big data has spurred broader network analysis. Using a comprehensive genealogical database of 30 million individuals in the Western Hemisphere, we analyzed how familial background, gender, and occupational domains influence the probability of individuals attaining “fame” in their lifetime. Individuals with famous parents are more likely to become famous themselves, especially males. Unexpectedly, we identified more than 10,000 famous individuals who are closely related, comprising 25.6% of all famous individuals in the entire dataset. While the underlying sample may have representativeness issues, the findings suggest that famous individuals are not isolated figures but often part of broader interconnected family networks. Full article

Recent advancements in Mobile Eye-Tracking (MET) technology have enabled the detailed examination of visitors’ embodied visual behaviors as they navigate exhibition spaces. This study employs MET to investigate visual attention patterns in an archeological museum, with a particular focus on identifying “hotspots” of attention. Through a multi-phase research design, we explore the relationship between visitor gaze behavior and museum learning experiences in a real-world setting. Using three key eye movement metrics—Time to First Fixation (TFF), Average Fixation Duration (AFD), and Total Fixation Duration (TFD), we analyze the distribution of visual attention across predefined Areas of Interest (AOIs). Time to First Fixation varied substantially by element, occurring most rapidly for artifacts and most slowly for labels, while video screens showed the shortest mean latency but greatest inter-individual variability, reflecting sequential exploration and heterogeneous strategies toward dynamic versus static media. Total Fixation Duration was highest for video screens and picture panels, intermediate yet variable for artifacts and text panels, and lowest for labels, indicating that dynamic and pictorial content most effectively sustain attention. Finally, Average Fixation Duration peaked on artifacts and labels, suggesting in-depth processing of descriptive elements, and it was shortest on video screens, consistent with rapid, distributed fixations in response to dynamic media. The results provide novel insights into the spatial and contextual factors that influence visitor engagement and knowledge acquisition in museum environments. Based on these findings, we discuss strategic implications for museum research and propose practical recommendations for optimizing exhibition design to enhance visitor experience and learning outcomes. Full article

Phubbing refers to the act of ignoring someone in a face-to-face conversation by paying more attention to a mobile phone. This practice, although common, has been identified as harmful and deserves to be considered a problem. As a result, it has captured the attention of the scientific community, with a significant increase in studies in recent years. However, there is a lack of bibliometric analyses examining existing research on this topic, which would be useful in guiding future studies. This article seeks to fill that gap by providing a detailed bibliometric analysis of phubbing. It analyses the entire production in the Web of Science database between 1985 and 2022 (199 articles and 82 keywords). The study employs citation, co-occurrence, and co-citation analysis techniques using scientific maps created with VOSviewer software. The results indicate that most research has focused on how phubbing affects romantic relationships, with less emphasis on other types of relationships such as family, work, or friends. Areas that require further research are identified, such as motivations for internet use, the tendency to compare oneself on social networks, impulsivity, and the influence of executive functions on phubbing. Further exploration of the relationship between phubbing and other disorders is also suggested. This analysis will serve as a guide and stimulus for future research, offering valuable resources for professionals in psychology, health, and education. Full article

With the global emphasis on sustainable growth and development, the depletion of natural energy reserves due to reliance on fossil fuels and non-renewable sources remains a critical concern. Despite strides in transitioning to electrical mobility, rural and agricultural communities depend heavily on liquefied petroleum gas and firewood for cooking, lacking viable, sustainable alternatives. This study focuses on community-led efforts to advance biogas adoption, providing an eco-friendly and reliable energy alternative for rural and farming households. By designing and developing balloon-type anaerobic biodigesters, this initiative provides a robust, cost-effective, and scalable method to convert farm waste into biogas for household cooking. This approach reduces reliance on traditional fuels, mitigating deforestation and improving air quality, and generates organic biofertilizer as a byproduct, enhancing agricultural productivity through organic farming. The study focuses on optimizing critical parameters, including the input feed rate, gas production patterns, holding time, biodigester health, gas quality, and liquid manure yield. Statistical tools, such as descriptive analysis, regression analysis, and ANOVA, were employed to validate and predict biogas output data based on experimental and industrial-scale data. Artificial neural networks (ANNs) were also utilized to model and predict outputs, inspired by the information processing mechanisms of biological neural systems. A comprehensive database was developed from experimental and literary data to enhance model accuracy. The results demonstrate significant improvements in cooking practices, health outcomes, economic stability, and solid waste management among beneficiaries. The integration of statistical analysis and ANN modeling validated the biodigester system’s effectiveness and scalability. This research highlights the potential to harness renewable energy to address socio-economic challenges in rural areas, paving the way for a sustainable, equitable future by fostering environmentally conscious practices, clean energy access, and enhanced agricultural productivity. Full article

Kutno is a picturesque city in central Poland, known for extensive rose breeding worldwide. Soil samples and rose petals were collected from 13 locations in the city and characterized by diverse environments. This allowed determining the response of plants to changing cultivation conditions. Rose petals have found a wide range of applications. They are used in the food, pharmaceutical and cosmetic industries. The aim of the research was to assess the contents of Cu, Zn, Cd, Ni, Pb and Cr in soils and their accumulation in rose petals. Samples were subjected to the microwave mineralization process using a mixture of concentrated HCl and HNO₃. The metal contents in the soil and roses were determined by HR-CS-AAS and ICP-OES, respectively. Roses are usually cultivated in soils with a limited mobile fraction of heavy metals. In these unfavorable conditions, flower petals can absorb heavy metals substantially. Petals of roses cultivated for cosmetic, pharmaceutical or food purposes should be tested for heavy metal content. This study indicates that toxic metals are blocked at the root zone, and their transport to the above-ground parts is severely hampered. Nevertheless, metals related to the photo-synthesis process (Zn, Cu) are more intensively taken up by roses, while the uptake of toxic metals is partially inhibited. Full article

In the automotive industry, battery electric vehicles (BEVs) represent the future of individual mobility. To establish a long-term market presence, innovative vehicle and powertrain concepts are essential, and therefore, identifying the most promising concepts is crucial to determine where to focus research and development further. Academia plays a significant role in this identification process; however, researchers often face restricted access to data from the industry, and identifying different technological approaches is often connected to significant costs. We present a comprehensive study of the Porsche Taycan Performance Battery Plus, which integrates two technological advancements: the first series-production implementation of a two-speed transmission in an electric vehicle allowing for high acceleration while reaching high top speeds and a 800 V battery system architecture providing more efficient charging capabilities. This study details vehicle dynamics, electric powertrain efficiencies, their impact on vehicle level, and the two technological advancements. This work aims to provide researchers access to vehicle dynamometer and real-world data from one of the most advanced and innovative battery electric sports cars. This allows for further analysis of cutting-edge technologies that have yet to reach the mass market. In addition to providing researchers with this study’s results, all data utilized in this study will be made available as open-access, enabling individual use of test data for parameter identification and the development of simulation models. Full article