Search Results (2,539)

High-precision positioning is the foundation for the functionality of various intelligent agents. In complex environments, such as urban canyons, relative pose estimation using cameras is a crucial step in high-precision positioning. To take advantage of the ability of an inertial measurement unit (IMU) to provide relatively accurate gravity prior information over a short period, we propose a minimal solution method for the relative pose estimation of a stereo camera system assisted by the IMU. We rigidly connect the IMU to the camera system and use it to obtain the rotation matrices in the roll and pitch directions for the entire system, thereby reducing the minimum number of corresponding points required for relative pose estimation. In contrast to classic pose-estimation algorithms, our method can also calculate the camera focal length, which greatly expands its applicability. We constructed a simulated dataset and used it to compare and analyze the numerical stability of the proposed method and the impact of different levels of noise on algorithm performance. We also collected real-scene data using a drone and validated the proposed algorithm. The results on real data reveal that our method exhibits smaller errors in calculating the relative pose of the camera system compared with two classic reference algorithms. It achieves higher precision and stability and can provide a comparatively accurate camera focal length. Full article

Background: Tobacco use is disproportionately high in rural areas, contributing to elevated cancer mortality, yet it often goes untreated due to limited access to care, high poverty and uninsured rates, and co-occurring substance use disorders (SUDs). This study explored the utility of using rapid qualitative analysis (RQA) to guide the adaptation of a tobacco-free workplace program (TFWP) in Texas healthcare centers serving adults with SUDs in medically underserved areas. Methods: From September–December 2023 and May–July 2024, we conducted 11 pre-implementation, virtual semi-structured group interviews focused on adapting the TFWP to local contexts (N = 69); 7 with providers (n = 34) and managers (n = 12) and 4 with patients (n = 23) in 6 healthcare centers. Two qualified analysts independently summarized transcripts, using RQA templates of key domains drawn from interview guides to summarize and organize data in matrices, enabling systematic comparison. Results: The main themes identified were minimal organizational tobacco cessation support and practices, and attitudinal barriers, as follows: (1) the need for program materials tailored to local populations; (2) limited tobacco cessation practices and partial policies—staff requested guidance on enhancing tobacco screenings and cessation delivery, and integrating new interventions; (3) contradictory views on treating tobacco use that can inhibit implementation (e.g., wanting to quit yet anxious that quitting would cause SUD relapse); and (4) inadequate environmental supports—staff requested treating tobacco-use training, patients group cessation counseling; both requested nicotine replacement therapy. Conclusions: RQA identified key areas requiring capacity development through participants’ willingness to adopt the following adaptations: program content (e.g., trainings and tailored educational materials), delivery methods/systems (e.g., adopting additional tobacco care interventions) and implementation strategies (e.g., integrating tobacco cessation practices into routine care) critical to optimizing TFWP fit and implementation. The study findings can inform timely formative evaluation processes to design and tailor similar intervention efforts by addressing site-specific needs and implementation barriers to enhance program uptake. Full article

The increasing demand for high-resolution subsurface imaging has driven significant advances in geophysical inversion methodologies. Despite the availability of various software packages for electrical resistivity tomography (ERT), time-domain induced polarization (TDIP), and seismic refraction tomography (SRT), significant challenges remain in selecting optimal regularization parameters and in the effective incorporation of prior information into the inversion process. In this study, we propose new strategies to address these critical issues by developing versatile and flexible tools for electrical and seismic tomographic data inversion. Specifically, we introduce two automated procedures for regularization parameter selection: a full loop method (fixed-λ optimization) where the regularization parameter is kept constant during the inversion process, and a single-inversion approach (automaticLam) where it varies throughout the iterations. Additionally, we present a novel constrained inversion strategy that effectively balances prior information, minimizes data misfit, and promotes model smoothness. This approach is thoroughly compared with the state-of-the-art methods, demonstrating its superiority in maintaining model reliability and reducing dependence on subjective operator choices. Applications to synthetic, laboratory, and real-world case studies validate the efficacy of our strategies, showcasing their potential to enhance the robustness of geophysical models and standardize the inversion process, ensuring its independence from operator decisions. Full article

The accurate early-stage estimation of environmental load (EL) and construction cost (CC) in road infrastructure projects remains a significant challenge, constrained by limited data and the complexity of construction activities. To address this, our study proposes a machine learning-based predictive framework utilizing artificial neural networks (ANNs) and deep neural networks (DNNs), enhanced by autoencoder-driven feature selection. A structured dataset of 150 completed national road projects in South Korea was compiled, covering both planning and design phases. The database focused on 19 high-impact sub-work types to reduce noise and improve prediction precision. A hybrid imputation approach—combining mean substitution with random forest regression—was applied to handle 4.47% missing data in the design-phase inputs, reducing variance by up to 5% and improving data stability. Dimensionality reduction via autoencoder retained 16 core variables, preserving 97% of explanatory power while minimizing redundancy. ANN models benefited from cross-validation and hyperparameter tuning, achieving consistent performance across training and validation sets without overfitting (MSE = 0.06, RMSE = 0.24). The optimal ANN yielded average error rates of 29.8% for EL and 21.0% for CC at the design stage. DNN models, with their deeper architectures and dropout regularization, further improved performance—achieving 27.1% (EL) and 17.0% (CC) average error rates at the planning stage and 24.0% (EL) and 14.6% (CC) at the design stage. These results met all predefined accuracy thresholds, underscoring the DNN’s advantage in handling complex, high-variance data while the ANN excelled in structured cost prediction. Overall, the synergy between deep learning and autoencoder-based feature selection offers a scalable and data-informed approach for enhancing early-stage environmental and economic assessments in road infrastructure planning—supporting more sustainable and efficient project management. Full article

In response to the increasing demand for environmentally friendly products and the parallel rise of deceptive green marketing practices, this study examines the impact of greenwashing awareness and green perceived benefits on consumers’ propensity to purchase green products, with a focus on the mediating role of green consumer confusion. Drawing upon data collected from 300 consumers in Greece through an online questionnaire, this study employed validated measurement scales and used multiple regression analyses to test its hypotheses. The findings reveal that both greenwashing awareness and green perceived benefits positively influence green purchase propensity. Additionally, green consumer confusion mediates the relationship between greenwashing awareness and green purchase propensity, indicating that the awareness of greenwashing reduces confusion and enhances consumers’ likelihood to choose genuinely green products. This study contributes to the literature by offering an integrated model that connects greenwashing awareness, green consumer confusion, and green perceived benefits in shaping green purchase propensity. Finally, the findings offer valuable insights for organizations to design clearer, more trustworthy green marketing strategies that minimize consumer confusion and foster informed green purchasing decisions. Full article

Objective: Mandibular expansion remains controversial due to concerns about long-term stability and effectiveness. While maxillary expansion protocols are well established, investigations into mandibular expansion remain limited. This study evaluates the efficacy of the Clara Expander appliance for mandibular expansion in early mixed dentition, assessing skeletal and dental changes using cone-beam computed tomography (CBCT). Materials and Methods: This prospective longitudinal study was conducted in Valencia, Spain, with a population of healthy children aged 6–10 years presenting negative osseodental mandibular discrepancies. CBCT scans were performed before and after treatment to evaluate mandibular dimensional changes, with statistical analyses conducted and a significance threshold of p < 0.05. A total of seven subjects were included in this case series, allowing for a descriptive analysis of treatment outcomes within this specific clinical context. Results: CBCT analysis confirmed significant mandibular expansion following the Clara Expander protocol. Post-treatment findings showed statistically significant increases in dental parameters, including Tooth 6 (furcation, MD = −2.25; p = 0.015), Tooth E (furcation, cementoenamel junction, vestibular, lingual, all p < 0.001), Tooth D (all variables significant), and Tooth C (furcation, MD = −4.18; p = 0.002; cementoenamel junction, MD = −3.56; p = 0.015). Conclusions: The Clara Expander appliance effectively promotes skeletal and dental mandibular expansion, with minimal adverse effects. Its user-friendly, non-invasive design enhances patient compliance and outcomes, contributing valuable data to the field of mandibular expansion and informing future research and clinical applications. Full article

This study aimed to monitor the operating state of an induction motor, a type of electromagnetic motor, using a highly sensitive magnetic sensor, which could be applied for anomaly detection in the future. Monitoring the health of electromagnetic motors is very important to minimize losses due to failures. Detecting anomalies using the changes compared with the initial state is a possible solution, but there are issues such as a lack of training data for machine learning and the need to install multiple sensors. Therefore, an attempt was made to acquire the various operating states of a motor from magnetic signals using a single magnetic sensor capable of non-contact measurement. The relationships between the magnetic flux density from the motor and the other motor conditions were investigated. As a result, the magnetic spectrum was found to contain information on the rotor rotation frequency, torque, and output power. Therefore, the magnetic sensor can be applied to monitor a motor’s operating conditions, making it a useful tool for advanced data analysis. Full article

To overcome the limitations in the perception performance of individual robots and homogeneous robot teams, this paper presents a distributed multi-robot collaborative SLAM method based on air–ground cross-domain cooperation. By integrating environmental perception data from UAV and UGV teams across air and ground domains, this method enables more efficient, robust, and globally consistent autonomous positioning and mapping. First, to address the challenge of significant differences in the field of view between UAVs and UGVs, which complicates achieving a unified environmental understanding, this paper proposes an iterative registration method based on semantic and geometric features assistance. This method calculates the correspondence probability of the air–ground loop closure keyframes using these features and iteratively computes the rotation angle and translation vector to determine the coordinate transformation matrix. The resulting matrix provides strong initialization for back-end optimization, which helps to significantly reduce global pose estimation errors. Next, to overcome the convergence difficulties and high computational complexity of large-scale distributed back-end nonlinear pose graph optimization, this paper introduces a multi-level partitioning majorization–minimization DPGO method incorporating loss kernel optimization. This method constructs a multi-level, balanced pose subgraph based on the coupling degree of robot nodes. Then, it uses the minimization substitution function of non-trivial loss kernel optimization to gradually converge the distributed pose graph optimization problem to a first-order critical point, thereby significantly improving global pose estimation accuracy. Finally, experimental results on benchmark SLAM datasets and the GRACO dataset demonstrate that the proposed method effectively integrates environmental feature information from air–ground cross-domain UAV and UGV teams, achieving high-precision global pose estimation and map construction. Full article

Ransomware, a significant cybersecurity threat, encrypts files and causes substantial damage, making early detection crucial yet challenging. This paper introduces a novel multi-phase framework for early ransomware detection, designed to enhance accuracy and minimize false positives. The framework addresses the limitations of existing methods by integrating operational data with situational and threat intelligence, enabling it to dynamically adapt to the evolving ransomware landscape. Key innovations include (1) data augmentation using a Bi-Gradual Minimax Generative Adversarial Network (BGM-GAN) to generate synthetic ransomware attack patterns, addressing data insufficiency; (2) Incremental Mutual Information Selection (IMIS) for dynamically selecting relevant features, adapting to evolving ransomware behaviors and reducing computational overhead; and (3) a Deep Belief Network (DBN) detection architecture, trained on the augmented data and optimized with Uncertainty-Aware Dynamic Early Stopping (UA-DES) to prevent overfitting. The model demonstrates a 4% improvement in detection accuracy (from 90% to 94%) through synthetic data generation and reduces false positives from 15.4% to 14%. The IMIS technique further increases accuracy to 96% while reducing false positives. The UA-DES optimization boosts accuracy to 98.6% and lowers false positives to 10%. Overall, this framework effectively addresses the challenges posed by evolving ransomware, significantly enhancing detection accuracy and reliability. Full article

Radio frequency fingerprint identification (RFFI) leverages the unique characteristics of radio signals resulting from inherent hardware imperfections for identification, making it essential for applications in telecommunications, cybersecurity, and surveillance. Despite the advancements brought by deep learning in enhancing RFFI accuracy, challenges persist in model deployment, particularly when transferring RFFI models across different receivers. Variations in receiver hardware can lead to significant performance declines due to shifts in data distribution. This paper introduces the source-free cross-receiver RFFI (SCRFFI) problem, which centers on adapting pre-trained RF fingerprinting models to new receivers without needing access to original training data from other devices, addressing concerns of data privacy and transmission limitations. We propose a novel approach called contrastive source-free cross-receiver network (CSCNet), which employs contrastive learning to facilitate model adaptation using only unlabeled data from the deployed receiver. By incorporating a three-pronged loss function strategy—minimizing information entropy loss, implementing pseudo-label self-supervised loss, and leveraging contrastive learning loss—CSCNet effectively captures the relationships between signal samples, enhancing recognition accuracy and robustness, thereby directly mitigating the impact of receiver variations and the absence of source data. Our theoretical analysis provides a solid foundation for the generalization performance of SCRFFI, which is corroborated by extensive experiments on real-world datasets, where under realistic noise and channel conditions, that CSCNet significantly improves recognition accuracy and robustness, achieving an average improvement of at least 13% over existing methods and, notably, a 47% increase in specific challenging cross-receiver adaptation tasks. Full article

Route planning under uncertain traffic conditions requires accounting for not only expected travel times but also the risk of late arrivals. This study proposes a mean-upper partial moment (MUPM) framework for pathfinding that explicitly considers travel time unreliability. The framework incorporates a benchmark travel time to measure the upper partial moment (UPM), capturing both the probability and severity of delays. By adjusting a risk parameter ($\theta$), the model reflects different traveler risk preferences and unifies several existing reliability measures, including on-time arrival probability, late arrival penalty, and semi-variance. A bi-objective model is formulated to simultaneously minimize mean travel time and UPM. Theoretical analysis shows that the MUPM framework is consistent with the expected utility theory (EUT) and stochastic dominance theory (SDT), providing a behavioral foundation for the model. To efficiently solve the model, an SDT-based label-correcting algorithm is adapted, with a pre-screening step to reduce unnecessary pairwise path comparisons. Numerical experiments using GPS probe vehicle data from Louisville, Kentucky, USA, demonstrate that varying $\theta$ values lead to different non-dominated paths. Lower $\theta$ values emphasize frequent small delays but may overlook excessive delays, while higher $\theta$ values effectively capture the tail risk, aligning with the behavior of risk-averse travelers. The MUPM framework provides a flexible, behaviorally grounded, and computationally scalable approach to pathfinding under uncertainty. It holds strong potential for applications in traveler information systems, transportation planning, and network resilience analysis. Full article

Background/Objectives: Access to specialized care for patients with cognitive impairment in remote areas is often limited. Despite the increasing adoption of telemedicine, standardized guidelines have not yet been specified. This study aimed to develop a comprehensive protocol for the specialized neurological, neuropsychological, and neuropsychiatric assessment of patients with cognitive disorders in remote areas through telemedicine. Methods: We analyzed data from (i) a comprehensive literature review of the existing recommendations, reliability studies, and telemedicine models for cognitive disorders, (ii) insights from a three-year experience of a specialized telemedicine outpatient clinic for cognitive movement disorders in Greece, and (iii) suggestions coming from dementia specialists experienced in telemedicine (neurologists, neuropsychologists, psychiatrists) who took part in three focus groups. A critical synthesis of the findings was performed in the end. Results: The final protocol included: technical and organizational requirements (e.g., a high-resolution screen and a camera with zoom, room dimensions adequate for gait assessment, a noise-canceling microphone); medical history; neurological, neuropsychiatric, and neuropsychological assessment adapted to videoconferencing; ethical–legal aspects (e.g., data security, privacy, informed consent); clinician–patient interaction (e.g., empathy, eye contact); diagnostic work-up; linkage to other services (e.g., tele-psychoeducation, caregiver support); and instructions for treatment and follow-up. Conclusions: This protocol is expected to serve as an example of good clinical practice and a source for official telemedicine guidelines for cognitive disorders. Ultimate outcomes include the potential enhanced access to specialized care, minimized financial and logistical costs, and the provision of a standardized, effective model for the remote diagnosis, treatment, and follow-up. This model could be applied not only in Greece, but also in other countries with similar healthcare systems and populations living in remote, difficult-to-access areas. Full article

Background/Objectives: Dental cavity preparation is a critical procedure in restorative dentistry that involves the removal of decayed tissue while preserving a healthy tooth structure. Excessive stress during tooth preparation leads to enamel cracking, dentin damage, and long term compressive pulp health. This study employed finite element analysis (FEA) to investigate the stress distribution in dental structures during cavity preparation using round diamond burs of varying diameters and depths of cut (DOC). Methods: A three-dimensional human maxillary first molar was generated from computed tomography (CT) scan data using 3D Slicer, Fusion 360, and ANSYS Space Claim 2024 R-2. Finite element analysis (FEA) was conducted using ANSYS Workbench 2024. Round diamond burs with diameters of 1, 2, and 3 mm were modeled. Cutting simulations were performed for DOC of 1 mm and 2 mm. The burs were treated as rigid bodies, whereas the dental structures were modeled as deformable bodies using the Cowper–Symonds model. Results: The simulations revealed that larger bur diameters and deeper cuts led to higher stress magnitudes, particularly in the enamel and dentin. The maximum von Mises stress was reached at 136.98 MPa, and dentin 140.33 MPa. Smaller burs (≤2 mm) and lower depths of cut (≤1 mm) produced lower stress values and were optimal for minimizing dental structural damage. Pulpal stress remained low but showed an increasing trend with increased DOC and bur size. Conclusions: This study provides clinically relevant guidance for reducing mechanical damage during cavity preparation by recommending the use of smaller burs and controlled cutting depths. The originality of this study lies in its integration of CT-based anatomy with dynamic FEA modeling, enabling a realistic simulation of tool–tissue interaction in dentistry. These insights can inform bur selection, cutting protocols, and future experimental validations. Full article

Chemical modifications are the standard for small interfering RNAs (siRNAs) in therapeutic applications, but predicting their off-target effects remains a significant challenge. Current approaches often rely on sequence-based encodings, which fail to fully capture the structural and protein–RNA interaction details critical for off-target prediction. In this study, we developed a framework to generate reproducible structure-based chemical features, incorporating both molecular fingerprints and computationally derived siRNA–hAgo2 complex structures. Using an RNA-Seq off-target study, we generated over 30,000 siRNA–gene data points and systematically compared nine distinct types of feature representation strategies. Among the datasets, the highest predictive performance was achieved by Dataset 3, which used extended connectivity fingerprints (ECFPs) to encode siRNA and mRNA features. An energy-minimized dataset (7R), representing siRNA–hAgo2 structural alignments, was the second-best performer, underscoring the value of incorporating reproducible structural information into feature engineering. Our findings demonstrate that combining detailed structural representations with sequence-based features enables the generation of robust, reproducible chemical features for machine learning models, offering a promising path forward for off-target prediction and siRNA therapeutic design that can be seamlessly extended to include any modification, such as clinically relevant 2′-F or 2′-OMe. Full article

Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generation increase but require predictive models that are both accurate and computationally efficient for real-time implementation. This paper proposes a data-driven observer to rapidly estimate the potential power gain achievable through AWC as a function of the ambient wind direction. The approach is rooted in Koopman operator theory, which allows a linear representation of nonlinear dynamics. Specifically, a model is developed using an Input–Output Extended Dynamic Mode Decomposition framework combined with Sparse Identification (IOEDMDSINDy). This method lifts the low-dimensional wind direction input into a high-dimensional space of observable functions and then employs iterative sparse regression to identify a minimal, interpretable linear model in this lifted space. By training on offline simulation data, the resulting observer serves as an ultra-fast surrogate model, capable of providing instantaneous predictions to inform online control decisions. The methodology is demonstrated and its performance is validated using two case studies: a 9-turbine and a 20-turbine wind farm. The results show that the observer accurately captures the complex, nonlinear relationship between wind direction and power gain, significantly outperforming simpler models. This work provides a key enabling technology for advanced, real-time wind farm control systems. Full article