Search Results (28,371)

In this paper, the development and performance evaluation of a 4WD robot system designed to follow near-distance moving objects using a 2D LiDAR sensor are presented. The study incorporates identifier (ID) classification and a distance-based dynamic angle of perception model to enhance the tracking capabilities of the 2D LiDAR sensor. A particle filter algorithm was utilized to verify the accuracy of object tracking. Furthermore, a proportional–derivative (PD) controller was designed and implemented to ensure the stability of the robot during operation. The experimental results demonstrate the potential applicability of these approaches in various industrial applications. Full article

Efficient cleaning is crucial in aquaculture vessels; however, Remotely Operated Vehicles (ROVs) encounter difficulties in regard to trajectory tracking within confined chambers, because of structural nonlinearities and environmental disturbances. To address these challenges, this paper proposes a multi-scale dynamic sliding mode adaptive control (MDSMAC) scheme to compensate for the effects of structural nonlinearities and external disturbances, achieving precise trajectory tracking. Based on a six -degree-of-freedom motion model, an adaptive multi-scale sliding mode control mechanism is designed, enabling the system to adapt to scale variations and environmental disturbances, enhancing control accuracy and robustness. The asymptotic stability of the system is rigorously proven using the second Lyapunov method. The numerical simulation results show that the proposed method exhibits superior robustness to external disturbances and high precision in complex environments, confirming its long-term stability. Water tank experiments were conducted to further evaluate the trajectory tracking performance of the method under nonlinear system control. The results show the high level of feasibility and strong potential of the approach for practical applications. Full article

This paper focuses on the design and development of a customized 7-axis suspended robotic arm for automated spraying production lines. The design process considers factors such as workspace dimensions, workpiece sizes, and suspension positions. After analyzing degrees of freedom and workspace coordinates, 3D modeling ensures the arm can reach designated positions and orientations. Servo motors and reducers are selected based on load capacity and speed requirements. A suspended body method allows flexible use within the workspace. Kinematics analysis is conducted, followed by trajectory-tracking experiments using the manifold deformation control method. Results from simulation and real experiments show minimal error in tracking, demonstrating the effectiveness of the control method. Finally, the actual coating thickness sprayed by the 7-axis suspended robotic arm at four locations on the motorcycle shell was measured. The results show that the measured values at each location fall within the standard range provided by the manufacturer, demonstrating consistency in spraying across different regions. This consistency highlights the precision and effectiveness of the robotic arm’s control system in achieving uniform coating thickness, even on complex and curved surfaces. Therefore, the robotic arm has been successfully applied in a factory’s automated spraying production line. Full article

Here we describe the synthesis and evaluation of a molecular corrosion sensor that can be applied in situ in aerospace coatings, then used to detect corrosion after the coating has been applied. A pH-sensitive molecule, 4-mercaptopyridin (4-MP), is attached to a gold nanoparticle to allow surface-enhanced Raman-scattering (SERS) for signal amplification. These SERS nanoparticles, when combined with an appropriate micron-sized carrier system, are incorporated directly into an MIL-SPEC coating and used to monitor the process onset and progression of corrosion using pH changes occurring at the metal–coating interface. The sensor can track corrosion spatially as it proceeds underneath the coating, due to the mobility of the proton front generated during corrosion and the homogeneous distribution of the sensor in the coating layer. To our knowledge, this report is the first time a 4-MP functionalized gold nanoparticle has been used, along with SERS spectroscopy, to monitor corrosion in an applied commercial coating in a fast, non-contact way. Full article

This study presents an approach to autonomous navigation for wheeled robots, combining radar-based dynamic obstacle detection with a BiGRU-based deep reinforcement learning (DRL) framework. Using filtering and tracking algorithms, the proposed system leverages radar sensors to cluster object points and track dynamic obstacles, enhancing precision by reducing noise and fluctuations. A BiGRU-enabled DRL model is introduced, allowing the robot to process sequential environmental data and make informed decisions in dynamic and unpredictable environments, achieving collision-free paths and reaching the goal. Simulation and experimental results validate the proposed method’s efficiency and adaptability, highlighting its potential for real-world applications in dynamic scenarios. Full article

Durian, often regarded as the “king of fruits”, plays a significant role in Thailand’s economy, with durian production expanding rapidly due to its profitability and high demand in both domestic and international markets. This growth has introduced challenges, particularly for geographic indication (GI)-certified durians like those from Pak Chong, where the unique soil, climate, and cultivation practices contribute to the fruit’s distinctive quality. Maintaining these standards is crucial to preserving GI certification, but farmers face increasing risks related to pests, diseases, climate variability, and cultivation practices. Effective risk management is essential to ensure the quality and sustainability of GI-certified durian production. This study analyzes risks in Pak Chong GI durian cultivation and proposes strategies to mitigate these risks. The House of Risk (HOR) method was used to identify potential risks at various stages of durian cultivation, including planting, maintenance, pre-harvest, harvest, and postharvest, and to recommend proactive mitigation strategies. This case study focuses on Pak Chong GI durian farmers. Thirty-one risk events driven by 17 risk agents were identified throughout the durian cultivation process. Key risk agents included observation of durian tree behavior, physical characteristics of the planting area, irrigation quantity, understanding of nutrient management, soil nutrients, and soil pH. The three most significant mitigation strategies identified were the implementation of targeted training and learning programs, improved data collection and plating progress tracking ability, and investment in advanced cultivation technology. This study analyzes the critical risks in Pak Chong GI-certified durian cultivation and proposes targeted mitigation strategies using the House of Risk (HOR) method. By identifying risks (HOR1) and developing proactive solutions (HOR2) across key cultivation stages, this research offers practical insights to enhance the quality and sustainability of GI-certified durian production. The findings aim to support farmers, policymakers, and stakeholders in preserving the economic and cultural value of Pak Chong durians. Full article

With the increasing complexity of urban roads and rising traffic flow, traffic safety has become a critical societal concern. Current research primarily addresses drivers’ attention, reaction speed, and perceptual abilities, but comprehensive assessments of cognitive abilities in complex traffic environments are lacking. This study, grounded in cognitive science and neuropsychology, identifies and quantitatively evaluates ten cognitive components related to driving decision-making, execution, and psychological states by analyzing video footage of drivers’ actions. Physiological data (e.g., Electrocardiogram (ECG), Electrodermal Activity (EDA)) and non-physiological data (e.g., Eye Tracking (ET)) are collected from simulated driving scenarios. A dual-branch Transformer network model is developed to extract temporal features from multimodal data, integrating these features through a weight adjustment strategy to predict driving-related cognitive abilities. Experiments on a multimodal driving dataset from the Computational Physiology Laboratory at the University of Houston, USA, yield an Accuracy (ACC) of 0.9908 and an F1-score of 0.9832, confirming the model’s effectiveness. This method effectively combines scale measurements and driving behavior under secondary tasks to assess cognitive abilities, providing a novel approach for driving risk assessment and traffic safety strategy development. Full article

Manipulator systems are increasingly deployed across various industries to perform complex, repetitive, and hazardous tasks, necessitating high-precision control for optimal performance. However, the design of effective control algorithms is challenged by nonlinearities, uncertain dynamics, disturbances, and varying real-world conditions. To address these issues, this paper proposes an advanced orbit-tracking control approach for manipulators, leveraging advancements in Time-Delay Estimation (TDE) and Fixed-Time Sliding Mode Control techniques. The TDE approximates the robot’s unknown dynamics and uncertainties, while a novel nonsingular fast terminal sliding mode (NFTSM) surface and novel fixed-time reaching control law (FTRCL) are introduced to ensure faster convergence within a fixed time and improved accuracy without a singularity issue. Additionally, an innovative auxiliary system is designed to address input saturation effects, ensuring that system states converge to zero within a fixed time even when saturation occurs. The Lyapunov-based theory is employed to prove the fixed-time convergence of the overall system. The effectiveness of the proposed controller is validated through simulations on a 3-DOF SAMSUNG FARA AT2 robot manipulator. Comparative analyses against NTSMC, NFTSMC, and GNTSMC methods demonstrate superior performance, characterized by faster convergence, reduced chattering, higher tracking accuracy, and a model-free design. These results underscore the potential of the proposed control strategy to significantly enhance the robustness, precision, and applicability of robotic systems in industrial environments. Full article

In this study, we present a novel approach using amperometric microsensors to detect quercetin in cosmetic formulations and track its metabolic behavior after topical application. This method offers a sensitive, real-time alternative to conventional techniques, enabling the detection of quercetin’s bioavailability, its transformation into active metabolites, and its potential therapeutic effects when applied to the skin. Quercetin (Q) is a bioactive flavonoid known for its potent antioxidant properties, naturally present in numerous plants, particularly those with applications in cosmetic formulations. In response to the growing interest in developing novel plant-based dermo-cosmetic solutions, this study investigates the electrochemical detection of quercetin, a ketone-type flavonoid, extracted from Gingko biloba essential oil. Three newly designed amperometric microsensors were developed to assess their efficacy in detecting quercetin in botanical samples. The sensor configurations utilized two forms of carbon material as a foundation: graphite (G) and carbon nanoparticles (CNs). These base materials were modified with paraffin oil, chitosan (CHIT), and cobalt(II) tetraphenylporphyrin (Co(II)TPP) to enhance sensitivity. Differential pulse voltammetry (DPV) served as the analytical method for this investigation. Among the sensors, the CHIT/G–CN microsensor exhibited the highest sensitivity, with a detection limit of 1.22 × 10⁻⁷ mol L⁻¹, followed by the G–CN (5.64 × 10⁻⁸ mol L⁻¹) and Co(II)TPP/G–CN (9.80 × 10⁻⁸ mol L⁻¹) microsensors. The minimum detectable concentration was observed with the G–CN and CoP/G–CN microsensors, achieving a threshold as low as 0.0001 μmol L⁻¹. Recovery rates and relative standard deviation (RSD) values averaged 97.4% ± 0.43, underscoring the sensors’ reliability for quercetin detection in botanical matrices. Full article

Low-frequency ocean noise (50–500 Hz) was recorded by a single omnidirectional hydrophone in the open waters of the Chukchi Plateau from 31 August 2021 to 6 September 2021 (local time). After other non-wind interference was filtered out, wind-generated noise source levels (NSLs) were extracted from the wind-generated noise. The correlation coefficients between the one-third octave wind-generated NSLs and sea surface wind speed exceed 0.84, an improvement of approximately 10% compared to those between the raw data and the wind speed. For 200–500 Hz, the wind-generated NSLs are highly consistent with Wilson’s (1983) estimated curve. The 50–300 Hz results closely match those of Chapman and Cornish (1993) from vertical line array (VLA) measurements. Both demonstrate the feasibility of extracting wind-generated NSLs by utilizing a single omnidirectional hydrophone in the Chukchi Plateau’s open waters. Furthermore, the research results of wind speed dependence and frequency dependence can be applied to calculate wind-generated NSLs in the Chukchi Plateau. Wind-derived ocean ambient noise data are useful for background correction in underwater target detection, recognition, tracking, and positioning. Full article

Introduction—Aim: Adopting a lifestyle that incorporates regular physical activity confers substantial benefits to both physical and mental health and is recommended for prediabetic individuals. The aim of this study is to investigate the impact of activity tracking apps on increasing physical activity and its effect on glycemic control in people with prediabetes. Materials and Methods: This pilot study included 37 participants, 18 in the prediabetic group and 19 in the normoglycemic group matched for age and gender (mean age 53 years, 40% males). Participants used the Google Fit app for 3 months. The number of daily steps was recorded via the app, and blood and urine tests as well as body fat measurements were conducted before and following 3 months of app use. The co-primary outcome was the change in steps, and the change in HbA1c in both groups. Secondary outcomes were the change in fasting plasma glucose (FPG) (main secondary outcome), as well as lipid parameters, body mass index, visceral fat, and kidney function indices among the two groups. Results: Both groups increased the daily step count following the app intervention, without any statistically significant difference when we compared the steps change between the two groups. We found a statistically significant difference between HbA1c levels in favor of the prediabetic compared to the normoglycemic group [mean difference (MD) 0.16, 95%CI 0.04, 0.28, p-value 0.01)], following the 3-month intervention. Additionally, there was a statistically significant difference between FPG levels in favor of the prediabetic compared to the normoglycemic group (MD 9.06, 95%CI 4.16, 13.96, p-value 0.001). Conclusions: The present study suggests that the use of activity tracking apps, combined with tailored activity goals and monthly supportive phone calls, may contribute to improved glycemic control among individuals with prediabetes. Full article

Cirrhotic cardiomyopathy (CCM) is a diagnostic entity defined as cardiac dysfunction (diastolic and/or systolic) in patients with liver cirrhosis, in the absence of overt cardiac disorder. Pathogenically, CCM stems from a combination of systemic and local hepatic factors that, through hemodynamic and neurohormonal changes, affect the balance of cardiac function and lead to its remodeling. Vascular changes in cirrhosis, mostly driven by portal hypertension, splanchnic vasodilatation, and increased cardiac output alongside maladaptively upregulated feedback systems, lead to fluid accumulation, venostasis, and cardiac dysfunction. Autocrine and endocrine proinflammatory cytokines (TNF-alpha, IL-6), as well as systemic endotoxemia stemming from impaired intestinal permeability, contribute to myocardial remodeling and fibrosis, which further compromise the contractility and relaxation of the heart. Additionally, relative adrenal insufficiency is often present in cirrhosis, further potentiating cardiac dysfunction, ultimately leading to the development of CCM. Considering its subclinical course, CCM diagnosis remains challenging. It relies mostly on stress echocardiography or advanced imaging techniques such as speckle-tracking echocardiography. Currently, there is no specific treatment for CCM, as it vastly overlaps with the treatment of heart failure. Diuretics play a central role. The role of non-selective beta-blockers in treating portal hypertension is established; however, their role in CCM remains somewhat controversial as their effect on prognosis is unclear. However, our group still advocates them as essential tools in optimizing the neurohumoral pathologic axis that perpetuates CCM. Other targeted therapies with direct anti-inflammatory and antioxidative effects still lack sufficient evidence for wide approval. This is not only a review but also a comprehensive distillation of the insights from practicing clinical hepatologists and other specialties engaged in advanced approaches to treating liver disease and its sequelae. Full article

The dialing-type authentication as a common PIN code input system has gained popularity due to the simple and intuitive design. However, this type of system has the security risk of “shoulder surfing attack”, so that attackers can physically view the device screen and keypad to obtain personal information. Therefore, based on the use of “Leap Motion” device and “Media Pipe” solutions, in this paper, we try to propose a new two-factor dialing-type input authentication system powered by aerial hand motions and features without contact. To be specific, based on the design of the aerial dialing system part, as the first authentication part, we constructed a total of two types of hand motion input subsystems using Leap Motion and Media Pipe, separately. The results of FRR (False Rejection Rate) and FAR (False Acceptance Rate) experiments of the two subsystems show that Media Pipe is more comprehensive and superior in terms of applicability, accuracy, and speed. Moreover, as the second authentication part, the user’s hand features (e.g., proportional characteristics associated with fingers and palm) were used for specialized CNN-LSTM model training to ultimately obtain a satisfactory accuracy. Full article

Ion radiotherapy requires accurate relative biological effectiveness (RBE) calculations to account for the markedly different biological effects of ions compared to photons. Microdosimetric RBE models rely on descriptions of the energy deposition at the microscopic scale, either through radial dose distributions (RDDs) or microdosimetric probability density distributions. While RDD approaches focus on the theoretical description of the energy deposition around the ion track, microdosimetric distributions offer the advantage of being experimentally measurable, which is crucial for quality assurance programs. As the results of microdosimetric RBE models depend on whether RDD or microdosimetric distributions are used, the model parameters are not interchangeable between these approaches. This study presents and validates a method to reproduce the published reference biological and clinical dose calculations at NIRS-QST for only carbon ion radiotherapy by using the modified microdosimetric kinetic model (MKM) alongside microdosimetric distributions instead of the reference RDD approach. To achieve this, Monte Carlo simulations were performed to estimate the variation of the radiation quality within and outside the field of pristine and spread-out Bragg peaks. By appropriately optimizing the modified MKM parameters for microdosimetric distributions assessed within water spheres, we successfully reproduced the results of calculations using the reference NIRS-QST RDD, generally within 2%. Full article

Background and Aims: Soft drink consumption is suspected to negatively impact bone health in children, but longitudinal evidence is limited. This study assessed the association between soft drink intake and bone health outcomes in Danish schoolchildren aged 7.7–12 years, within a physical activity intervention framework. Methods: This study was nested in the CHAMPS-DK trial, a quasi-experimental study. Participants (n = 529) were recruited from intervention schools offering 270 min of physical education (PE) per week (active arm) and control schools with 90 min of standard PE. Soft drink intake was assessed via a food-frequency questionnaire at baseline. Dual-energy X-ray absorptiometry (DXA) was used to measure Bone Mineral Content (BMC), Bone Area (BA), and Bone Mineral Density (BMD) at baseline and two-year follow-up (primary outcomes). Fracture incidence over a five-year period was recorded using the SMS-Track parental reporting system (secondary outcome). Multilevel mixed-effects linear regression and Weibull survival models were used to analyze associations. Results: Soft drink intake of more than twice per month did not significantly affect BMC, BA, or BMD over two years (Total body BMD: β = 0.004; 95% CI: (−0.007; 0.016). Adjustment for confounders such as age, sex, BMI, pubertal status, socioeconomic status, and physical activity did not change the results. Additionally, no significant difference in fracture risk was observed (HR = 0.86; 95% CI: [0.43; 1.71]). Conclusions: Soft drink intake had no measurable impact on bone health indices or fracture risk in children, irrespective of PE intervention. Future studies should investigate the effects of specific soft drink types (carbonated vs. non-carbonated) on bone development. Full article