Search Results (36)

The recent integration of Unmanned Ground Vehicles (UGVs) into human activities represents a significant scientific advancement and technological development, with substantial impacts across various fields, not limited to mechanical engineering, including agriculture, defense, and civil construction. Therefore, this study aims to provide a practical methodological framework, developed through a historical and systematic literature review, to emphasize the general criteria and the main interactions that an engineer should consider in the initial design phase of a UGV, thereby subsequently proceeding with its computer-aided modeling and simulation. To this end, a systematic literature review is conducted to identify current research interests in this field and pinpoint potential research gaps. Following the systematic literature review presented in this study, the focus of the present investigation shifts to classifying UGVs by analyzing their characteristics based on specific criteria, including weight, type of steering system, and wheel and track configurations. Additionally, the differences between wheels and tracks are further examined by comparing these two solutions and highlighting their advantages and limitations. This review paper also addresses power systems, hardware components, and navigation challenges. Subsequently, the primary sectors and applications where these vehicles are widely utilized are thoroughly analyzed. Finally, a specific section of the manuscript is dedicated to illustrating the preliminary mechanical design of a typical unmanned ground vehicle, thereby highlighting its functional requirements and selecting the most suitable locomotion system. For this purpose, preliminary evaluations and simple calculations are introduced to determine the motor performance required for the proposed design example. In conclusion, the literature survey on UGVs presented in this paper, rooted in the common perspective of kinematic and dynamic analysis of multibody mechanical systems, clearly highlights the importance of this topic in modern engineering applications. Full article

We introduce local matching stability and furthest matchable frame as quantitative measures for evaluating the success of underwater image enhancement. This enhancement process addresses visual degradation caused by light absorption, scattering, marine growth, and debris. Enhanced imagery plays a critical role in downstream tasks such as path detection and autonomous navigation for underwater vehicles, relying on robust feature extraction and frame matching. To assess the impact of enhancement techniques on frame-matching performance, we propose a novel evaluation framework tailored to underwater environments. Through metric-based analysis, we identify strengths and limitations of existing approaches and pinpoint gaps in their assessment of real-world applicability. By incorporating a practical matching strategy, our framework offers a robust, context-aware benchmark for comparing enhancement methods. Finally, we demonstrate how visual improvements affect the performance of a complete real-world algorithm—Simultaneous Localization and Mapping (SLAM)—reinforcing the framework’s relevance to operational underwater scenarios. Full article

The rapid development of deep learning (DL) has demonstrated its modular contributions to artificial intelligence (AI) techniques, such as large language models (LLMs). DL variants have proliferated across domains such as feature extraction, normalization, lightweight architecture design, and module integration, yielding substantial advancements in these subfields. However, the absence of a unified review framework to contextualize DL’s modular evolutions within AI development complicates efforts to pinpoint future research directions. Existing review papers often focus on narrow technical aspects or lack systemic analysis of modular relationships, leaving gaps in our understanding how these innovations collectively drive AI progress. This work bridges this gap by providing a roadmap for researchers to navigate DL’s modular innovations, with a focus on balancing scalability and sustainability amid evolving AI paradigms. To address this, we systematically analyze extensive literature from databases including Web of Science, Scopus, arXiv, ACM Digital Library, IEEE Xplore, SpringerLink, Elsevier, etc., with the aim of (1) summarizing and updating recent developments in DL algorithms, with performance benchmarks on standard dataset; (2) identifying innovation trends in DL from a modular viewpoint; and (3) evaluating how these modular innovations contribute to broader advances in artificial intelligence, with particular attention to scalability and sustainability amid shifting AI paradigms. Full article

Autonomous vehicles, submersible robotic systems and drones, and other human-carrying equipment consistently adhere to a safety perimeter, ensuring collision-free navigation amidst surrounding objects. In contrast, roller coaster vehicles, despite being constrained to a predetermined track, necessitate frequent safety distance detection owing to the variability introduced by trees and decorative installations. Passengers’ limbs may protrude beyond vehicle boundaries, posing a collision hazard. The motion range of limbs, influenced by vehicle-specific conditions, mismatches standardized safety volumes (cylindrical, cubic, and rectangular) designed for mobile entities. The roller coaster industry’s current practice involves a moving safety frame, which visually inspects for collisions to assess safety distances, which is cumbersome and prone to oversight in intricate settings. Therefore, this study introduces a novel safety envelope detector (SE-detector). It creates a customer-defined virtual safety envelope around the roller coaster vehicle and measures the safety distance based on LiDAR (Light Detection and Ranging) to detect the intrusions of obstacles. Meanwhile, this SE-detector also innovatively integrated an accelerometer to synchronously measure the acceleration of the vehicle. The measured acceleration will be aligned with simulated sequences by dynamic time warping (DTW) algorithms to pinpoint intrusion location. Additionally, a wide-angle camera is also deployed to enhance perception of the surrounding environment. The SE-detector developed in this study has the capability to record inspection results. It is expected to enhance the inspection capabilities of the safety envelope for roller coasters, thereby improving the efficiency of safety distance inspection. Full article

Sound source localization (SSL) adds a spatial dimension to auditory perception, allowing a system to pinpoint the origin of speech, machinery noise, warning tones, or other acoustic events, capabilities that facilitate robot navigation, human–machine dialogue, and condition monitoring. While existing surveys provide valuable historical context, they typically address general audio applications and do not fully account for robotic constraints or the latest advancements in deep learning. This review addresses these gaps by offering a robotics-focused synthesis, emphasizing recent progress in deep learning methodologies. We start by reviewing classical methods such as time difference of arrival (TDOA), beamforming, steered-response power (SRP), and subspace analysis. Subsequently, we delve into modern machine learning (ML) and deep learning (DL) approaches, discussing traditional ML and neural networks (NNs), convolutional neural networks (CNNs), convolutional recurrent neural networks (CRNNs), and emerging attention-based architectures. The data and training strategy that are the two cornerstones of DL-based SSL are explored. Studies are further categorized by robot types and application domains to facilitate researchers in identifying relevant work for their specific contexts. Finally, we highlight the current challenges in SSL works in general, regarding environmental robustness, sound source multiplicity, and specific implementation constraints in robotics, as well as data and learning strategies in DL-based SSL. Also, we sketch promising directions to offer an actionable roadmap toward robust, adaptable, efficient, and explainable DL-based SSL for next-generation robots. Full article

Retrieving specific, often instantaneous, content from hours-long egocentric video footage based on hazily remembered details is challenging. Vision–language models (VLMs) have been employed to enable zero-shot textual-based content retrieval from videos. But, they fall short if the textual query contains ambiguous terms or users fail to specify their queries enough, leading to vague semantic queries. Such queries can refer to several different video moments, not all of which can be relevant, making pinpointing content harder. We investigate the requirements for an egocentric video content retrieval framework that helps users handle vague queries. First, we narrow down vague query formulation factors and limit them to ambiguity and incompleteness. Second, we propose a zero-shot, user-centered video content retrieval framework that leverages a VLM to provide video data and query representations that users can incrementally combine to refine queries. Third, we compare our proposed framework to a baseline video player and analyze user strategies for answering vague video content retrieval scenarios in an experimental study. We report that both frameworks perform similarly, users favor our proposed framework, and, as far as navigation strategies go, users value classic interactions when initiating their search and rely on the abstract semantic video representation to refine their resulting moments. Full article

Global digitalization drives policy-led transformation in the construction industry, yet its effectiveness hinges on localized implementation. However, research on China’s regional digital policies remains insufficient, particularly in systematic evaluation mechanisms. Focusing on Wuhan, this study proposes a progressive “3M” (macro–meso–micro) policy evaluation framework to analyze local policy efficacy under national strategies. Macro-level PESTEL analysis identifies weak legal frameworks as a critical gap. Meso-level PMC index modeling establishes a hierarchical optimization pathway prioritizing incentive measures, followed by policy timeliness, assessment mechanisms, policy focus, and policy nature. Micro-level Spearman’s correlation analysis further pinpoints five implementation drivers: pilot projects, long-term planning, detailed measures, talent cultivation, and regulatory reinforcement. The results indicate that Wuhan’s policies require targeted improvements: (1) synergizing pilot innovation with legal safeguards, (2) integrating green principles into long-term planning, (3) refining technical standards and policy alignment, (4) enhancing multidisciplinary talent development through industry–academia collaboration, and (5) establishing IoT-enabled dynamic monitoring platforms. This hierarchical evaluation system provides empirical evidence for optimizing China’s construction policies while offering a transferable governance framework for global cities navigating digital transitions. Future research should extend the temporal and spatial coverage while incorporating adaptive evaluation tools to address policy dynamism. Full article

This paper presents an autonomous end-to-end guidance, navigation, and control (GNC) solution for a reusable launcher, addressing the challenges of precision pinpoint landing and reusability. The proposed GNC system integrates advanced onboard trajectory optimization and $H_{\infty }$ control to ensure robust performance across re-entry, aerodynamics, and landing phases. This work discusses the GNC design and definition and introduces the strategies adopted both for the guidance and the control design to handle rapidly varying dynamic environments and strict landing requirements. Particular attention is given to design choices in the guidance optimization problem and the control definition for each phase, which were made to enhance the harmonization of the guidance and control (G&C) system. The proposed GNC is integrated in a high-fidelity Functional Engineering Simulator (FES) and its robustness is assessed in a real-world scenario, considering a downrange landing mission of the RETALT1 (RETro propulsion Assisted Landing Technologies Two-Stage-To-Orbit vehicle) rocket. Full article

Background: Indocyanine green (ICG) fluorescence has seen extensive application across medical and surgical fields, praised for its real-time navigation capabilities and low toxicity. Initially employed to assess liver function, ICG fluorescence is now integral to liver surgery, aiding in tumor detection, liver segmentation, and the visualization of bile leaks. This study reviews current protocols and ICG fluorescence applications in liver surgery, with a focus on optimizing timing and dosage based on clinical indications. Methods: Following PRISMA guidelines, we systematically reviewed the literature up to 27 January 2024, using PubMed and Medline to identify studies on ICG fluorescence used in liver surgery. A systematic review was performed to evaluate dosage and timing protocols for ICG administration. Results: Of 1093 initial articles, 140 studies, covering a total of 3739 patients, were included. The studies primarily addressed tumor detection (40%), liver segmentation (34.6%), and both (21.4%). The most common ICG fluorescence dose for tumor detection was 0.5 mg/kg, with administration occurring from days to weeks pre-surgery. Various near-infrared (NIR) camera systems were utilized, with the PINPOINT system most frequently cited. Tumor detection rates averaged 87.4%, with a 10.5% false-positive rate. Additional applications include the detection of bile leaks, lymph nodes, and vascular and biliary structures. Conclusions: ICG fluorescence imaging has emerged as a valuable tool in liver surgery, enhancing real-time navigation and improving clinical outcomes. Standardizing protocols could further enhance ICG fluorescence efficacy and reliability, benefitting patient care in hepatic surgeries. Full article

Background: Genetic and genomic literacy is pivotal in empowering cancer patients and citizens to navigate the complexities of omics sciences, resolve misconceptions surrounding clinical research and genetic/genomic testing, and make informed decisions about their health. In a fast-evolving scenario where routine testing has become widespread in healthcare, this scoping review sought to pinpoint existing gaps in literacy and understanding among cancer patients and the general public regarding genetics and genomics. Methods: Adhering to the PRISMA framework, the review included 43 studies published between January 2018 and June 2024, which evaluated the understanding of genetics and genomics among cancer patients, caregivers, and citizens. Results: Although the selected studies had significant heterogeneity in populations and evaluation tools, our findings indicate inadequate literacy levels, with citizens displaying lower proficiency than cancer patients and caregivers. This review highlighted consistent knowledge gaps in understanding the genetic and genomic underpinnings of diseases, encompassing misconceptions about mutation types and inheritance patterns, limited awareness of available genetic testing options, and difficulties in interpreting test results. Ethical and privacy concerns and the psychological impact of genetic testing were also common, highlighting the imperative need for effective communication between healthcare providers and patients. Conclusions: Given the dynamic nature of genomic science, the review underscores the need for continuously evolving educational programs tailored to diverse populations. Our findings could guide the development of educational resources addressed explicitly to cancer patients, caregivers, and the lay public. Full article

The Blue Economy, a dynamic field intertwining ocean sustainability, innovation, and economic progress, stands as a beacon of hope for fostering inclusive growth while advancing sustainable practices. This systematic literature review embarks on a journey to unravel the intricate relationship between innovation and sustainable practices within the Blue Economy, to uncover how innovation transforms and promotes sustainability, and to pinpoint barriers to adoption of innovative technologies and processes. By delving into the multifaceted landscape of sustainability and innovation studies within the Blue Economy, this study illuminates the potential of innovative approaches to drive sustainability in coastal and marine areas. With global attention shifting toward ocean sustainability due to survival risks and resource scarcity, this study addresses two central questions: how does innovation drive sustainable practices within the Blue Economy, and what barriers prevent the widespread adoption of these innovations? Using this interrogation as a compass to navigate the existing literature, and through a comprehensive analysis of the role of innovation in promoting sustainable practices, this review aims to provide hints for the main directions for a sustainable Blue Economy. Full article

The world’s population is aging and, as populations age, they exhibit an increased prevalence of chronic diseases, which can reduce the independence of elderly individuals. The set of initiatives known as aging in place, a common policy response to the aging population, is preferred by both the elderly population and policymakers. Aging in place is a broad and multifaceted topic that involves multiple stakeholders and academic disciplines. A science map of the literature on aging in place can help researchers pinpoint their efforts and help policymakers make informed decisions. Thus, this study maps the scientific landscape of the aging-in-place literature. This review used bibliometric analysis to examine 3240 publications on aging in place indexed in the Web of Science. Using VOSviewer 1.6.20, it conducted various analyses, including a citation analysis and an analysis of the co-occurrence of author-provided keywords. The study identified key research areas, leading countries, institutions, and journals, central publications, and the temporal evolution of themes in the literature. Based on its keyword co-occurrence analysis, the study identified five major research-area clusters: (1) aging-in-place facilitators, (2) age-friendly communities, (3) housing, (4) assistive technologies, and (5) mental health. This study improves the understanding of the various interdisciplinary factors that have influenced the research on aging in place. By making this research more accessible, the study can help researchers and policymakers navigate the extensive information on aging in place and complex relationships more effectively. Full article

Considerable variation in soil often occurs within and across production fields, which can significantly impact farming input management strategies. Optimizing resource utilization while enhancing crop productivity is critical for achieving Sustainable Development Goals (SDGs). This paper proposes a low-cost retrofittable Variable Rate Applicator Controller (VRAC) designed to leverage soil variability and facilitate the adoption of Variable Rate Technologies. The controller operates using a Raspberry Pi platform, RTK—Global Navigation Satellite System (GNSS), a stepper motor, and an anti-slip wheel encoder. The VRAC allows precise, on-the-fly control of the Variable Rate application of farming inputs utilizing an accurate GNSS to pinpoint geographic coordinates in real time. A wheel encoder measures accurate distance travel, providing a real-time calculation of speed with a slip-resistant wheel design for precise RPM readings. The Raspberry Pi platform processes the data, enabling dynamic adjustments of variability based on predefined maps, while the motor driver controls the motor’s RPM. It is designed to be plug-and-play, user-friendly, and accessible for a broader range of farming practices, including seeding rates, dry fertilizer, and liquid fertilizer application. Data logging is performed from various field sensors. The controller exhibits an average of 0.864 s for rate changes from 267 to 45, 45 to 241, 241 to 128, 128 to 218, and 218 to 160 kg/ha at speeds of 8, 11, 16, 19, 24, and 32 km/h. It has an average coefficient of variation of 4.59, an accuracy of 97.17%, a root means square error (RMSE) of 4.57, an R square of 0.994, and an average standard deviation of 1.76 kg for seeding discharge. The cost-effectiveness and retrofitability of this technology offer an increase in precision agriculture adoption to a broader range of farmers and promote sustainable farming practices. Full article

Operating a powered wheelchair involves significant risks and requires considerable cognitive effort to maintain effective awareness of the surrounding environment. Therefore, people with significant disabilities are at a higher risk, leading to a decrease in their social interactions, which can impact their overall health and well-being. Thus, we propose an intelligent driving-assistance system that innovatively uses Transformers, typically employed in Natural Language Processing, for navigation and a retrieval mechanism, allowing users to specify their destinations using natural language. The system records the areas visited and enables users to pinpoint these locations through descriptions, which will be considered later in the retrieval phase. Taking a foundational model, the system is fine-tuned with simulated data. The preliminary results demonstrate the system’s effectiveness compared to non-assisted solutions and its readiness for deployment on edge devices. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and affects millions of individuals globally. AD is associated with cognitive decline and memory loss that worsens with aging. A statistical report using U.S. data on AD estimates that approximately 6.9 million individuals suffer from AD, a number projected to surge to 13.8 million by 2060. Thus, there is a critical imperative to pinpoint and address AD and its hallmark tau protein aggregation early to prevent and manage its debilitating effects. Amyloid-β and tau proteins are primarily associated with the formation of plaques and neurofibril tangles in the brain. Current research efforts focus on degrading amyloid-β and tau or inhibiting their synthesis, particularly targeting APP processing and tau hyperphosphorylation, aiming to develop effective clinical interventions. However, navigating this intricate landscape requires ongoing studies and clinical trials to develop treatments that truly make a difference. Genome-wide association studies (GWASs) across various cohorts identified 40 loci and over 300 genes associated with AD. Despite this wealth of genetic data, much remains to be understood about the functions of these genes and their role in the disease process, prompting continued investigation. By delving deeper into these genetic associations, novel targets such as kinases, proteases, cytokines, and degradation pathways, offer new directions for drug discovery and therapeutic intervention in AD. This review delves into the intricate biological pathways disrupted in AD and identifies how genetic variations within these pathways could serve as potential targets for drug discovery and treatment strategies. Through a comprehensive understanding of the molecular underpinnings of AD, researchers aim to pave the way for more effective therapies that can alleviate the burden of this devastating disease. Full article