Search Results (3,996)

Aiming at the high-precision trajectory tracking problem of the new surface and underwater joint observation system (SUJOS) in the ocean remote sensing monitoring mission under complex sea conditions, especially at the problem of excessive tracking errors and slow convergence of actual trajectory oscillations caused by the wide range of angular changes in the motion trajectory, a real-time optimization improved model predictive control (IMPC) trajectory tracking method based on fuzzy control is proposed. Initially, the novel observation platform has been designed, and its mathematical model has been systematically established. In addition, this study optimizes the MPC trajectory tracking framework by integrating the least squares adaptive algorithm and the Extended Alternating Direction Method of Multipliers (EADMM). In addition, a fuzzy controller, optimized using a genetic algorithm, an output of real-time optimization coefficients, is employed to dynamically adjust and optimize the bias matrix within the objective function of the IMPC. Consequently, the real-time performance and accuracy of the system’s trajectory tracking are significantly enhanced. Ultimately, through comprehensive simulation and practical experimental verification, it is demonstrated that the real-time optimization IMPC algorithm exhibits commendable real-time and optimization performance, which markedly enhances the accuracy for trajectory tracking, and further validates the stability of the controller. Full article

Background. The SARS-CoV-2 pandemic had a significant impact on sexual health and human behavior, revealing a widespread decline in sexual function and behaviors. Objective. To summarize these findings and highlight their importance for public health, this article discusses the changes observed in sexual function and behavior during the pandemic, as well as potential explanations for these trends. Methods. This study followed the PRISMA-ScR guidelines, using the keyword search commands: “sexual function” AND (“SARS-CoV-2” OR “COVID-19” OR coronavirus) and “sexual behavior*” AND (“SARS-CoV-2” OR “COVID-19” OR coronavirus) in the Scopus and PubMed databases. The search was conducted on 10 March 2024, including articles published from January 2019 to March 2024. Inclusion criteria required studies focusing on sexual health/function during the SARS-CoV-2 pandemic, excluding non-English articles and non-adult populations. Studies were screened based on relevance, methodological rigor, and sample size, with data extraction focusing on sexual behavior/function metrics. Results were synthesized to identify trends and propose explanatory models. Results. While some individuals experienced reductions in sexual desire and activities, others reported increases, indicating varied individual responses to stressors such as a pandemic. Two hypotheses are presented to explain these changes: terror management theory and the dual control model of sexual response. The critical role of public health in addressing sexual health and well-being needs during a health crisis is discussed, emphasizing the importance of providing clear information, ensuring access to remote sexual health services, and reducing stigma. The need to integrate sexual health into the global response to future health crises is highlighted to ensure a comprehensive approach to human well-being. Conclusions. This review shows the multifaceted impact of the pandemic and social distancing in people’s sexual function and behaviors, underscoring the importance of considering sexual health as an integral part of the emergency health planning and response, to promote the physical and mental well-being of the population during crises such as the SARS-CoV-2 pandemic. Full article

With greenhouse gas emissions and climate change continuing to be major global concerns, researchers are increasingly focusing on reducing energy consumption as a key strategy to address these challenges. In recent years, various devices and technologies have been developed for residential buildings to implement energy-saving strategies and enhance energy efficiency. This paper presents a real-time IoT-based smart monitoring system designed to optimize energy consumption and enhance residents’ safety through efficient monitoring of home conditions and appliance usage. The system is built on a Raspberry Pi Model 4B as its core platform, integrating various IoT sensors, including the DS18B20 for temperature monitoring, the BH1750 for measuring light intensity, a passive infrared (PIR) sensor for motion detection, and the MQ7 sensor for carbon monoxide detection. The Adafruit IO platform is used for both data storage and the design of a graphical user interface (GUI), enabling residents to remotely control their home environment. Our solution significantly enhances energy efficiency by monitoring the status of lighting and heating systems and notifying users when these systems are active in unoccupied areas. Additionally, safety is improved through IFTTT notifications, which alert users if the temperature exceeds a set limit or if carbon monoxide is detected. The smart home monitoring device is tested in a university residential building, demonstrating its reliability, accuracy, and efficiency in detecting and monitoring various home conditions. Full article

Breast cancer survivors experience numerous chronic symptoms linked to autonomic dysfunction including anxiety, stress, insomnia, menopausal symptoms, and cognitive impairment. Effective non-pharmacological solutions to address these are currently lacking. Methods: Our three-armed longitudinal randomized controlled trial assessed the effectiveness of a 4-week remote smartphone-based heart rate variability biofeedback intervention which involved daily paced breathing at 6 breaths p/min; active (12 breaths p/min) and waitlist controls were included. Heart rate variability and self-reported cancer-related symptoms were assessed at baseline, post-, and 6 months-post intervention. Participants were 60 UK-based women with primary breast cancer history (6 to 60 months post-active treatment). Results: The intervention group showed significant increases in low-frequency heart rate variability over time (F (4, 103.89) = 2.862, p = 0.027, d = 0.33), long-lasting improvement in sleep quality (F (4, 88.04) = 4.87, p = 0.001, d = 0.43) and cessations in night sweats (X² (2, N = 59) = 6.44, p = 0.04, Cramer’s V = 0.33), and reduced anxiety post-intervention compared to the active and waitlist controls (F (4, 82.51) = 2.99, p = 0.023, d = 0.44). Other findings indicated that the intervention and active control participants reported lasting improvements in cognitive function, fatigue, and stress-related symptoms (all ps < 0.05). The waitlist group reported no symptom changes across time. Conclusion: Heart rate variability biofeedback is a feasible intervention for addressing diverse chronic symptoms commonly reported by breast cancer survivors. Full article

Cognitive abilities are foundational to complex tasks, which may be also important in complex project-based teaching. However, the role of teachers’ cognitive abilities in project-based teaching is still unknown. Therefore, this study aimed to explore the relationship between teachers’ cognitive abilities and project-based teaching using a mixed methods design. In Study 1, a quantitative regression analysis was conducted with 62 primary school teachers. They completed the project-based teaching questionnaire and performed four cognitive tasks: remote association (creativity), object detail memory (object detail processing ability), paper folding (spatial ability), and sentence comprehension (verbal ability). Regression analysis revealed that spatial ability significantly predicted a teacher’s project-based teaching ability, even after controlling for age, gender, teaching experience, and project-based teaching experience. In Study 2, a qualitative exploratory case study was employed to examine how spatial ability manifests in two teachers’ project-based teaching plans. The teacher with higher spatial ability used schemata, abstract concepts, a better overall plan, and a deeper understanding of mathematics than teachers with lower spatial ability. This study indicated that a teacher’s spatial ability is closely associated with their project-based teaching, and it provides a new perspective for teachers to cultivate project-based teaching. Full article

Life tables are indispensable in IPM, offering an analysis of insect population dynamics. These tables record survival rates, fecundity, and other parameters at various developmental stages, enabling the identification of key factors that affect population numbers and the prediction of growth trajectories. This review discusses the application of life tables in agricultural pest management, including the assessment of the pest control capacity of natural enemies, the evaluation of biological agents, and the screening of insect-resistant plant species. In vector insect control, life tables are used to evaluate the transmission risks, model the population dynamics, and interfere with the life cycles of vector insects. For invasive pests, life tables help us to monitor population dynamics and predict future population sizes. In chemical pest control, life tables assist in evaluating the fitness costs of pesticide resistance, guiding insecticide selection, and optimizing application timing. In the final section, we explore future research directions, emphasizing the potential of integrating new technologies such as genomics, ethology, and satellite remote sensing to enhance life table analysis and improve IPM strategies. Full article

This study introduces Lake SkyWater (LSW), a novel radiometric buoy designed for the reliable measurement of remote sensing reflectance (R_rs) in lakes using the Skylight-Blocked Approach (SBA). LSW addresses key challenges in “on-water” field radiometry owing to its motorised rotating system, which maintains the radiance sensor in optimal geometrical conditions (i.e., facing the sun). Our device is easy to transport and deploy and can be controlled with a smartphone over Wi-Fi. Its modular design, which uses standard components and custom 3D-printed parts, facilitates customisation. A field experiment demonstrated excellent performance in the visible spectrum (400–700 nm) and no significant differences compared with handheld SBA measurements when measuring R_rs (coefficient of determination > 0.99 and general accuracy (median symmetric accuracy) of ~2.43%). Areas for potential improvement were identified, such as refinement of orientation control and addressing the occasional rotation of the float. Nonetheless, LSW shortens the acquisition time, reduces the risk of fore-optics contamination, and ensures that the measurements are conducted under optimal geometric conditions. In conclusion, LSW is a promising instrument for the operational collection of high-quality R_rs spectra in lakes, which is important for advancing both research and monitoring applications in aquatic remote sensing. Full article

With the rapid development of unmanned aerial vehicle (UAV) manufacturing technology, large-scale UAV swarm ad hoc networks are becoming widely used in military and civilian spheres. UAV swarms equipped with ad hoc networks and satellite networks are being developed for 6G heterogeneous networks, especially in offshore and remote areas. A key operational aspect in large-scale UAV swarm networks is slot allocation for large capacity and a low probability of conflict. Traditional methods typically form coalitions among UAVs that are in close spatial proximity to reduce internal network interference, thereby achieving greater throughput. However, significant internal interference still persists. Given that UAV networks are required to transmit a substantial amount of safety-related control information, any packet loss due to internal interference can easily pose potential risks. In this paper, we propose a distributed time coalition formation game algorithm that ensures the absence of internal interference and collisions while sharing time slot resources, thereby enhancing the network’s throughput performance. Instead of forming a coalition from UAVs within a contiguous block area as used in prior studies, UAV nodes with no interference from each other form a coalition that can be called a time coalition. UAVs belonging to one coalition share their transmitting slots with each other, and thus, every UAV node achieves the whole transmitting slots of coalition members. They can transmit data packets simultaneously with no interference. In addition, a distributed coalition formation game-based TDMA (DCFG-TDMA) protocol based on the distributed time coalition formation algorithm is designed for UAV swarm ad hoc networks. Our simulation results verify that the proposed algorithm can significantly improve the UAV throughput compared with that of the conventional TDMA protocol. Full article

In recent years, the rapid development of remote tower technology has made it crucial to accurately assess the situational awareness (SA) levels of remote tower controllers. Such an assessment is significant for controller training and remote tower system design. This study employed the SART scale to compare controllers’ SA scores in traditional and remote tower environments. Results revealed significant differences, especially in attention demand and situational understanding. Subsequently, a quantitative analysis of controllers’ perception, understanding, and decision-making abilities was conducted, integrating subjective and objective data. Eye-tracking, heart rate, working memory scales, and communication-coordination scales showed significant results. Experienced controllers had better psychological safety skills, while trainees were more likely to increase vigilance. Moreover, a series of sensitive SA indicators were identified. An evaluation index system was established using the entropy weight method. By calculating the Euclidean distance, Gray relational degree, and comprehensive proximity coefficient, the SA levels of controllers were comprehensively evaluated. The top five important indicators were average blink rate, scan length, average fixation duration, fixation duration, and average pupil diameter. These findings support enhancing air traffic control safety and refining SA assessment for remote tower controllers. Full article

The COVID-19 pandemic had a disproportionate impact on American Indian and Alaska Native (“Native”) communities, including factors impacting alcohol-exposed pregnancy (AEP) risk. This is especially true for young Native women in urban settings, where over 70% of the population resides, yet their experiences are rarely accounted for in research. We conducted remote in-depth interviews from March to May 2022, roughly concurrent with the Omicron surge and relaxed lockdown measures, with a subsample of 15 urban Native young women ages 16–20 who were participating in a national randomized controlled trial of an AEP preventive intervention. Participants were asked how the pandemic affected their use of alcohol, sexual health, mental health, and relationships. A qualitative analysis revealed diverse experiences during the pandemic. While some participants experienced greater risks for AEP due to increased alcohol use and reduced access to birth control, other participants drank less alcohol and had greater access to birth control. Additionally, while some participants faced mental health challenges due to isolation and relational strains that emerged during the pandemic, others found the pandemic to be a time that afforded self-reflection, self-development, and a deepening of relationships. Full article

Remote driving, i.e., the capacity of controlling road vehicles at a distance, is an innovative transportation technology often associated with potential ethical benefits, especially when deployed to tackle urban traffic issues. However, prospected benefits could only be reaped if remote driving can be executed in a safe and responsible way. This paper builds on notions elaborated in the philosophical literature on technological mediation to offer a systematic examination of the extent to which current and emerging Human–Machine Interfaces contribute to hindering or supporting the exercise of responsibility behind the remote wheel. More specifically, the analysis discusses how video, audio, and haptic interfaces co-shape the remote driving experience and, at the same time, the operators’ capacity to drive responsibly. The multidisciplinary approach explored in this research offers a novel methodological framework to structure future empirical inquiries while identifying finely tuned multi-sensory HMIs and dedicated training as critical presuppositions to the remote drivers’ exercise of responsibility. Full article

Establishing a maintenance-free current sensing network across the entire power grid to facilitate wide-area online monitoring is crucial for realizing a smart grid. However, distribution networks (DNs) frequently lack effective real-time current monitoring owing to the complexity of load types, extensive line distribution, and numerous branches. In this study, we propose a high-precision, self-powered online current monitoring system that integrates a TMR sensors array module, a main control module, a current transformer (CT) power harvesting module, and current online monitoring software. The TMR sensors array module boasts a measurement range of 0–300 A and a high sensitivity of 25.38 mV/A. To address wire eccentricity errors in array sensors, we develop a neural network-based correction algorithm, which identifies wire positions and applies correction coefficients, achieving high accuracy with an average error of 1.23%. Current data are wirelessly transmitted to software terminals via 4G communication for remote monitoring. Furthermore, the CT power harvesting module converts magnetic energy from the power grid into electrical energy, ensuring that the system is self-powered. Validation through continuous 24-h monitoring of DNs demonstrates the system’s high precision and stability. This work presents an effective solution for high-accuracy online current monitoring in DNs. Full article

To address current challenges, such as the low visualization of workshops and difficulty in the remote control of equipment, which are supported by the theoretical system of the digital twin, a method of real-time monitoring and dynamic bidirectional interaction in workshops based on the digital twin is proposed. This method aims to realize high-fidelity mapping from the virtual workshop to the physical workshop, laying the groundwork for predicting the status of various production elements in the physical workshop. First, based on research on the theoretical knowledge of the digital twin workshop, a seven-dimensional model and maturity framework for the digital twin workshop are proposed. Building on this, the theoretical knowledge is integrated with practical applications to construct a comprehensive digital twin workshop system (DTWS) architecture. The key technologies involved in the real-time monitoring of virtual and physical workshops are described in detail, including the construction of digital twin workshop models, intelligent perception of multi-source heterogeneous data, virtual–physical interaction, and synchronous operation methods for virtual–physical workshops. Finally, a prototype system for the digital twin workshop is designed and developed to realize the real-time monitoring and remote control of the physical workshop. The feasibility and effectiveness of the proposed method are validated through enterprise applications. Full article

Understanding global patterns of tree canopy height and density is essential for effective forest management and conservation planning. This study examines how these attributes vary along latitudinal gradients and identifies key climatic drivers influencing them. We utilized high-resolution remote sensing datasets, including a 10 m resolution canopy height dataset aggregated to 1 km for computational efficiency, and a 1 km resolution tree density dataset derived from ground-based measurements. To quantify the relationships between forest structure and environmental factors, we applied nonlinear regression models and climate dependency analyses, incorporating bioclimatic variables from the WorldClim dataset. Our key finding is that latitude exerts a dominant but asymmetric control on tree height and density, with tropical regions exhibiting the strongest correlations. Tree height follows a quadratic latitudinal pattern, explaining 29.3% of global variation, but this relationship is most pronounced in the tropics (−10° to 10° latitude, R² = 91.3%), where warm and humid conditions promote taller forests. Importantly, this effect differs by hemisphere, with the Southern Hemisphere (R² = 67.1%) showing stronger latitudinal dependence than the Northern Hemisphere (R² = 35.3%), indicating climatic asymmetry in forest growth dynamics. Tree density exhibits a similar quadratic trend but with weaker global predictive power (R² = 7%); however, within the tropics, latitude explains 90.6% of tree density variation, underscoring strong environmental constraints in biodiverse ecosystems. Among climatic factors, isothermality (Bio 3) is identified as the strongest determinant of tree height (R² = 50.8%), suggesting that regions with stable temperature fluctuations foster taller forests. Tree density is most strongly influenced by the mean diurnal temperature range (Bio 2, R² = 36.3%), emphasizing the role of daily thermal variability in tree distribution. Precipitation-related factors (Bio 14 and Bio 19) moderately explain tree height (~33%) and tree density (~25%), reinforcing the role of moisture availability in structuring forests. This study advances forest ecology research by integrating high-resolution canopy structure data with robust climate-driven modeling, revealing previously undocumented hemispheric asymmetries and biome-specific climate dependencies. These findings improve global forest predictive models and offer new insights for conservation strategies, particularly in tropical regions vulnerable to climate change. Full article

The increasing demand for image quality in aerospace remote sensing has led to higher performance requirements for inertial stabilization platforms equipped with image sensors, particularly in terms of bandwidth. To achieve wide-bandwidth control in optical stabilization platforms, engineers employ magneto-hydrodynamic (MHD) sensors as key components to enhance system performance because of their wide measurement bandwidth (5–1000 Hz). While MHD sensors offer a wide-frequency response, they are limited by a narrow measuring range and low sensitivity at low frequencies, making them unsuitable as standalone sensors. To address the challenges of over-range measurement and the loss of low-frequency signals, in this study, we developed a soft-switching adaptive Kalman filter method, which enables us to dynamically adjust the fusion weights in the Kalman filter so we can obtain wide-band measurement signals even when the MHD sensor experiences over-range conditions. The proposed method was validated with fusion experiments involving a fiber-optic gyroscope and an MHD sensor; the results demonstrate its ability to expand the sensing bandwidth, regardless of the operating conditions of the MHD sensor. Full article