Presence Of Obstacles Research Articles

Spatial Distribution Pattern of Aromia bungii Within China and Its Potential Distribution Under Climate Change and Human Activity.

Aromia bungii is a pest that interferes with the health of forests and hinders the development of the fruit tree industry, and its spread is influenced by changes in abiotic factors and human activities. Therefore, exploring their spatial distribution patterns and potential distribution areas under such conditions is crucial for maintaining forest ecosystem security. This study analyzed the spatial differentiation characteristics of the geographic distribution pattern of A. bungii in China using Moran's I and the Getis-Ord General G index. Hot spot distribution areas were identified using Getis-Ord Gi*. An optimized MaxEnt model was used to predict the potential distribution areas of A. bungii within China under four shared economic pathways by combining multivariate environmental data: (1) prediction of natural environmental variables predicted under current climate models; (2) prediction of natural environmental variables + human activities under current climate models; and (3) prediction of natural environmental variables under the future climate models (2050s and 2070s). Meanwhile, MigClim was used to simulate the unoccupied suitable area in the presence of obstacles under future climate change. The results showed that human activities, minimum temperature of the coldest month, and precipitation of the wettest month had positive effects on the distribution of A. bungii. However, in the current period, human activities drastically reduced the survival area of A. bungii, and its suitable distribution area was mainly concentrated in the eastern and central regions of China. Under the influence of climate change in the future, the habitat of A. bungii will gradually increase. Additionally, the MigClim model indicates that the area unoccupied by A. bungii has been on a continuous increasing trend. This study provides a positive reference for the prevention and control of A. bungii and the maintenance of forest health and ecosystem security, and provides important theoretical guidance for researchers, policymakers, and governments.

Focus Group Interview: University-Industrial Collaboration

Business innovations can introduce novel and imaginative concepts, particularly within digitalization. Innovations have the potential to stimulate the advancement of enterprises, fostering market expansion, enhancing competitiveness, and optimizing profitability. Nevertheless, the implementation of innovation may be achievable in collaboration. Collaborations and interactions between academia and industrial entities may yield improved outcomes. Nevertheless, the presence of obstacles between academia and industries could harm the long-term viability of commercial innovation. The concerns and challenges faced by local institutions and enterprises in Malaysia are examined within the framework of a case study. Data was obtained from a random sample of Chief Executive Officers (CEOs) across several industries. A focus group was conducted with 15 representatives from Small and Medium Enterprise (SME) companies and one moderator to lead the discussion. The findings from the focus group have been transcribed and analyzed based on their sharing. The rationale for focusing on CEOs was their role as critical decision-makers responsible for driving corporate innovations. The collection of perspectives from academia and industries was facilitated through face-to-face interviews. The study provided suggestions for potential avenues of future investigation.

Comparison of ANN and XGBoost surrogate models trained on small numbers of building energy simulations.

Surrogate optimisation holds a big promise for building energy optimisation studies due to its goal to replace the use of lengthy building energy simulations within an optimisation step with expendable local surrogate models that can quickly predict simulation results. To be useful for such purpose, it should be possible to quickly train precise surrogate models from a small number of simulation results (10-100) obtained from appropriately sampled points in the desired part of the design space. Two sampling methods and two machine learning models are compared here. Latin hypercube sampling (LHS), widely accepted in building energy community, is compared to an exploratory Monte Carlo-based sequential design method mc-intersite-proj-th (MIPT). Artificial neural networks (ANN), also widely accepted in building energy community, are compared to gradient-boosted tree ensembles (XGBoost), model of choice in many machine learning competitions. In order to get a better understanding of the behaviour of these two sampling methods and two machine learning models, we compare their predictions against a large set of generated synthetic data. For this purpose, a simple case study of an office cell model with a single window and a fixed overhang, whose main input parameters are overhang depth and height, while climate type, presence of obstacles, orientation and heating and cooling set points are additional input parameters, was extensively simulated with EnergyPlus, to form a large underlying dataset of 729,000 simulation results. Expendable local surrogate models for predicting simulated heating, cooling and lighting loads and equivalent primary energy needs of the office cell were trained using both LHS and MIPT and both ANN and XGBoost for several main hyperparameter choices. Results show that XGBoost models are more precise than ANN models, and that for both machine learning models, the use of MIPT sampling leads to more precise surrogates than LHS.

A Quantitative Cross-Sectional Study on the Behavioral Intentions of Polish Medical and Healthcare Students Regarding Interprofessional Collaboration in Their Future Clinical Practice

Background/Objectives: Since the importance of robust theory-driven research is emphasized in medical education and little data are available on the intentions of medical and healthcare students regarding interprofessional collaboration, this study aimed to analyze the behavioral intentions of Polish medical and healthcare students to undertake interprofessional collaboration in their future work. This study follows the assumptions of the theory of planned behavior, including analysis of the students’ attitudes, subjective norms, and perceived behavioral control in this regard. Methods: Data were collected from March to July 2023 on the population of students at Poznan University of Medical Sciences (Poland) using a questionnaire developed using dedicated manuals on the theory. Results: The students demonstrated generally positive behavioral intentions and attitudes, with a mean total attitude score of 103.13 ± 33.31 in a possible range of −231 to 231. Their sense of social pressure to engage in interprofessional collaboration was weak to moderate positive, as indicated by their total subjective norm score equal on average to 57.01 ± 42.98 in a possible range of −189 to 189, or mixed when evaluated directly. Furthermore, even though they presented a neutral or moderately positive direct assessment of their perceived behavioral control, its indirect measure was weak to moderately negative, with a mean total perceived behavioral control of −80.78 ± 59.21 in a possible range of −231 to 231. Conclusions: The findings suggest that students’ perceptions of mixed social pressure and the presence of barriers or obstacles to collaboration may negatively impact their perceived ease and willingness to collaborate, even despite their initially positive attitudes towards it.

Addressing unpredictable movements of dynamic obstacles with deep reinforcement learning to ensure safe navigation for omni-wheeled mobile robot

The safe and efficient navigation of mobile robots in the presence of unknown dynamic obstacles remains a complex and unresolved challenge. This paper presents collision-free path planning for a mobile robot that safely deals with multi-directional obstacles, that is, randomly moving dynamic obstacles, using a Deep Reinforcement Learning (DRL) algorithm named Deep Q-Network (DQN) with inflated robot reward functions. The robot moves in a time-efficient and collision-free route while maintaining a safe distance with both static and unpredictable dynamic obstacles. The modified DQN algorithm takes RGB images of the environment as input for training a Convolution Neural Network (CNN) and provides a safe and short path for navigation. The robot used for training is an omni-wheeled mobile robot exploring outdoors, that is, concourse environment, and indoors, that is, home environment. The Closed-Loop Inverse Kinematics (CLIK) algorithm is employed to control a mobile robot to follow the desired path. The simulation results indicate that the proposed algorithm with inflated robot reward functions demonstrates remarkable performance as compared to recently used Reinforcement Learning (RL) algorithms when dealing with both stationary and randomly moving obstacles in the given environment.

Ad hoc test functions for homogenization of compressible viscous fluid with application to the obstacle problem in dimension two

In this paper, we highlight a set of ad hoc test functions to study the homogenization of viscous compressible fluids in domains with very tiny holes. This set of functions allows to improve previous results in dimensions two and three. As an application, we show that the presence of a small obstacle does not influence the dynamics of a viscous compressible fluid in dimension two.

Impact of urban road characteristics on vehicle speed: Insights from Brescia, Italy

In managing road infrastructures, a key benchmark is the 85th percentile of vehicle speeds (V85). While V85 can be derived from spot speed samples, these are often lacking on each urban road. Thus, prediction models become valuable tools for examining the relationship between V85 and road characteristics. Although various models exist for rural roads, the impact of roadside characteristics and markings on V85 in urban road networks has been partially investigated, and the effect of traffic calming measures remains fragmented.This study aims to address these gaps by applying a methodology that sheds light on the effects of some variables that influence V85 on urban roads. Specifically, the methodology selects and segments roads along the urban road network of the municipality of Brescia (Italy) and collects data on both road characteristics and 48,000+ spot speed information. Following data cleansing, it processes these data according to three different multiple regression models to analyse the influence of various predictors on V85. Once the best model is estimated, its performance is evaluated, and the final list of significant predictors is obtained.The results revealed that V85 increases with longer homogeneous segments, greater distance to successive intersections, bituminous conglomerate roads with more lanes, and the presence of trees, visible road markings, and posted speed limits. Conversely, V85 decreases in the presence of on-street parking and other obstacles (e.g., walls and road posts), when the density of road intersections and pedestrian crossings increases, when the left crossbar width increases and when the land use crossed is commercial or office, residential or industrial. Nevertheless, no significant effect was found for several traffic calming measures included in the model.These findings can assist road authorities in verifying road operating conditions and planning infrastructure interventions to reduce speeds, thereby creating a safer urban environment for all users.

Virtual Obstacle Avoidance Strategy: Navigating through a Complex Environment While Interacting with Virtual and Physical Elements.

Robotic walking devices can be used for intensive exercises to enhance gait rehabilitation therapies. Mixed Reality (MR) techniques may improve engagement through immersive and interactive environments. This article introduces an MR-based multimodal human-robot interaction strategy designed to enable shared control with a Smart Walker. The MR system integrates virtual and physical sensors to (i) enhance safe navigation and (ii) facilitate intuitive mobility training in personalized virtual scenarios by using an interface with three elements: an arrow to indicate where to go, laser lines to indicate nearby obstacles, and an ellipse to show the activation zone. The multimodal interaction is context-based; the presence of nearby individuals and obstacles modulates the robot's behavior during navigation to simplify collision avoidance while allowing for proper social navigation. An experiment was conducted to evaluate the proposed strategy and the self-explanatory nature of the interface. The volunteers were divided into four groups, with each navigating under different conditions. Three evaluation methods were employed: task performance, self-assessment, and observational measurement. Analysis revealed that participants enjoyed the MR system and understood most of the interface elements without prior explanation. Regarding the interface, volunteers who did not receive any introductory explanation about the interface elements were mostly able to guess their purpose. Volunteers that interacted with the interface in the first session provided more correct answers. In future research, virtual elements will be integrated with the physical environment to enhance user safety during navigation, and the control strategy will be improved to consider both physical and virtual obstacles.

Post-ACA health insurance decisions: A survey on risk attitudes and consumer confidence

Objective: This study investigates the reluctance of individuals in the United States to get health insurance, specifically examining the psychological and sociodemographic aspects that contribute to this reluctance within the Affordable Care Act (ACA) framework. The research objective is to ascertain the behavioral factors contributing to insurance hesitation and offer valuable perspectives for decreasing the number of individuals without health insurance and enhancing health security. Methodology: The study uses data from the 2022 Survey of Consumer Finances to analyze the impact of specific variables such as risk tolerance, consumer confidence, income, education, age, and marital status on health insurance coverage choices. We also address issues related to cost, perceived health requirements, previous experiences, and misunderstandings about eligibility. Conclusion: The study demonstrates that higher customer confidence, frequently associated with financial stability, decreases reluctance to obtain insurance. By contrast, individuals with more risk tolerance exhibit more hesitancy, opting to depend on internal resilience. Furthermore, substantial demographic disparities indicate the presence of several obstacles in acquiring comprehensive coverage. Implications and Recommendations: The results underscore the necessity of tailored policy interventions that address the unique requirements of different demographic groups. It is crucial to consider both psychological and sociodemographic aspects to promote insurance adoption and ensure universal health security. The present study makes a valuable contribution to the current body of literature by offering a comprehensive examination of health insurance hesitation following the Affordable Care Act (ACA) implementation.

Substrate heterogeneities cause root growth trajectories to switch from vertical to oblique.

Hard pans, soil compaction, soil aggregation and stones create physical barriers that can affect the development of a root system. Roots are known to exploit paths of least resistance to avoid such obstacles, but the mechanism through which this is achieved is not well understood. Here, we combined 3D-printed substrates with a high-throughput live imaging platform to study the responses of plant roots to a range of physical barriers. Using image analysis algorithms, we determined the properties of growth trajectories and identified how the presence of rigid circular obstacles affects the ability of a primary root to maintain its vertical trajectory. Results showed the types of growth responses were limited, both vertical and oblique trajectories were found to be stable and influenced by the size of the obstacles. When obstacles were of intermediate sizes, trajectories were unstable and changed in nature through time. We formalised the conditions for root trajectory to change from vertical to oblique, linking the angle at which the root detaches from the obstacle to the root curvature due to gravitropism. Exploitation of paths of least resistance by a root may therefore be constrained by the ability of the root to curve and respond to gravitropic signals.

Simulation Analysis of Thermoacoustic Effect of CNT Film with Metasurface-Enhanced Acoustic Autofocusing.

This study introduces a novel thermoacoustic (TA) focusing system enhanced by Airy beam-based acoustic metasurfaces, significantly improving acoustic focusing and efficiency. The system integrates a TA emitter, fabricated from carbon nanotube (CNT) films, with a binary acoustic metasurface capable of generating quasi-Airy beams. Through finite element simulations, the system's heat conduction, acoustic focusing, and self-healing properties were thoroughly analyzed. The results demonstrate that the system achieves superior sub-wavelength focusing, tunable focal length via frequency control, and robust self-healing, even in the presence of obstacles. These findings address current limitations in TA emitters and suggest broader applications in medical ultrasound and advanced technology.

Obstacle-aware path following of omni-wheeled robots using fuzzy inference approach

This study proposes a real-time LiDAR-based approach for efficient path following in omni-wheeled robots (OWRs) capable of dynamically adapting to surrounding obstacles. To achieve this, two fuzzy control schemes are developed: Fuzzy Sliding-Mode Tracking Control (FTC) for precise path following and Fuzzy Obstacle-avoidance Control (FOC) for real-time collision avoidance using LiDAR data. FTC utilizes fuzzified sliding surfaces, derived from tracking errors, to follow a planned path. In the presence of obstacles along the path, FOC, employing LiDAR measurements, assesses collision distance and direction, ensuring safe navigation. The outputs of FTC and FOC are seamlessly merged using a smooth switching factor to generate voltage inputs for the actuators. The effectiveness of the proposed approach is demonstrated through extensive simulations and real-world experiments under various conditions, including wheel disturbances, diverse trajectories, and varying initial positions.

Behavioral characteristics of bidirectional pedestrian-e-bike mixed flow at a signalized crosswalk: An experimental study

Mixed-traffic crosswalks are a prevalent scene in urban traffic. Understanding the movement characteristics of bidirectional e-bike-pedestrian mixed flow is helpful to enhance traffic efficiency at an intersection. In this research, a controlled experiment on bidirectional e-bike-pedestrian mixed flow was conducted. There are three variables, namely the proportion of e-bike riders (i.e., 0.5 and 0.3), the directional split ratio (namely, 1:1, 1:2 and 1:5) and the presence of obstacles on both sides of the crosswalk (i.e., yes and no). Petrack software were used to extract the participants’ trajectories. Passing time, detour ratio, selection preference, fundamental diagrams, the temporal and spatial characteristics and congestion level are analysed depending on the trajectories. It is discovered that a higher proportion of e-bike riders combined with a smaller directional split ratio contributes to increasing passing time of e-bike riders. With the decreasing directional split ratio, the average detour ratios of both pedestrians and e-bike riders decrease. With the decreasing proportion of e-bike riders, the average detour ratio of pedestrians increases, while that of e-bike riders decreases. The average crossing speed of the minor flow is greater than that of the major flow. With the decrease of the directional split ratio, the congestion level in the measurement area also reduces. Pedestrians in balanced flow are more likely to cause large congestion degree than e-bike riders. These results are beneficial for the design of crosswalks, pedestrian crossing management and the configuration of traffic signals.

Prescribed fire and natural habitat characteristics affect crawfish frog (Rana areolata) advertisement call transmission

ABSTRACT For acoustically signalling animals, the rate of signal degradation is critical for successful communication. Degradation is heavily influenced by habitat characteristics, with sound degrading more rapidly in the presence of environmental obstacles (e.g. vegetation). Land management strategies such as prescribed burns eliminate obstacles and should therefore lead to more efficient sound transmission. Crawfish frogs (Rana areolata) produce long-distance advertisement calls for breeding. Habitats surrounding crawfish frog breeding ponds vary in vegetative density, and prairies surrounding breeding habitat often undergo controlled burns prior to the breeding season. We hypothesised that R. areolata advertisement calls would transmit more efficiently in post-burned prairies compared to other more vegetated habitats. To test this hypothesis, we conducted a sound transmission playback experiment, broadcasting crawfish frog calls in three habitats: forest, prairie, and post-burned prairie. We compared sound degradation measures between habitat types and across distances. We found that crawfish frog calls experienced up to 6.8 dB less excess attenuation in post-burned habitats relative to other habitat types, and that in general, prairie habitats had better sound transmission than forest. These results indicate that land management and habitat structure have consequences for acoustic communication, and may also influence the findings of frog call detection surveys.

The effect of spatial non-uniformity on multiple transient modes of detonation onset in a three-dimensional channel

The study of the transient combustion modes is one of the key topics when considering the safety of space flights. Control of detonation onset has a dual application. First, the search for ways to prevent detonation modes in case of accidental fuel releases for fire safety issues of launch systems and the avoidance of accidents with rocket engines at a launch site and in near-Earth space. Second, the study of detonation and the possibility of using it to create propulsion systems based on detonation combustion of fuel. The paper shows the effect of the presence of spatial non-uniformities on the promotion of detonation in the chamber. Various geometries with and without obstacles and cavities are considered. It is demonstrated that the presence of obstacles accelerates the transition to the detonation process on the one hand, but on the other hand the presence of obstacles in combustion chamber could be the cause of incidental uncontrolled ignition, which ruins stable operation of an engine. The results of theoretical studies of the working cycle of the combustion chamber of a pulsed detonation engine are presented. Theoretical estimates for thrust characteristics are obtained.

Acoustic wave propagation in depth-evolving sound-speed field using the lattice Boltzmann method

This study investigates the propagation of sound waves within deep-sea low-sound-speed channels using the lattice Boltzmann method, with a key focus on the influence of depth-dependent sound speed on wave propagation. The depth-variable sound speed condition is realized through the incorporation of an external force proportional to the density gradient. After the model verification, investigations into the two-dimensional spreading of sound sources reveal that the depth-dependent sound speed curves the wave propagation. When source depths differing from the low-sound-speed channel, wave paths deviate due to contrasting speeds above and below. When the sound source is situated within the low-sound-speed channel, waves exhibit converging patterns. The simulations also detail the total reflection behavior of sound waves. When the incident angle falls exceeds the critical angle, the waves remain intact within the low-sound-speed channel, thereby enabling the preservation of high amplitude acoustic signals even at remote locations. The subsequent simulations of sound wave propagation around obstacles demonstrate that the low-sound-speed channel also exhibits better signal transmission capabilities in the presence of obstacles. In a uniform sound speed environment, acoustic wave propagation around a submarine exhibits a symmetric pattern. By contrast, under depth-evolving speed conditions, submarines operating at various depths manifest distinct propagation characteristics, such as asymmetric wave propagation during shallow diving, as well as wave attenuation or even silencing when cruising within low-sound-speed channels. These findings underscore the profound implications of depth-evolving sound speed on underwater acoustic signal detection and transmission.

Unilateral buckling of thin plates by complementarity eigenvalue analyses

In this work, the analysis by the finite element method of thin plates subjected to buckling in the presence of unilateral punctual obstacles, that is, supports that allow the plate to move in one direction but prevent the motion in the opposite direction, is addressed. An appropriate algorithm based on the solution of a semi-smooth system of equations, that results from the formulation of the unilateral buckling problem as a complementarity eigenvalue problem, is used. Rectangular and square plates are analysed under various membrane loadings, including compression and shear. The conformal Bogner–Fox–Schmit (BFS) finite element is employed to compute the bifurcation loads and the corresponding instability modes in scenarios with and without unilateral obstacles. For each plate and type of loading, the six lowest bifurcation loads and corresponding modes are computed for different levels of mesh refinement. The results confirm that the convergence of bifurcation loads obtained using the BFS element is monotonically decreasing as the mesh is refined. It is also confirmed that, when unilateral obstacles are present, the lowest bifurcation load, known as the critical load, can never be lower than the one of the homologous problem without unilateral obstacles.

Unlicensed Spectrum Access and Performance Analysis for NR-U/WiGig Coexistence in UAV Communication Systems

Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations necessitate substantial data and video transmission, requiring significant spectral resources. The unlicensed millimeter wave (mmWave) spectrum, especially in the 60 GHz frequency band, offers promising potential for UAV communications. However, WiGig users are the incumbent users of the 60 GHz unlicensed spectrum. Therefore, to ensure fair coexistence between UAV-based new radio-unlicensed (NR-U) users and WiGig users, unlicensed spectrum-sharing strategies need to be meticulously designed. Due to the beam directionality of the NR-U system, traditional listen-before-talk (LBT) spectrum sensing strategies are no longer effective in NR-U/WiGig systems. To address this, we propose a new cooperative unlicensed spectrum sensing strategy based on mmWave beamforming direction. In this strategy, UAV and WiGig users cooperatively sense the unlicensed spectrum and jointly decide on the access strategy. Our analysis shows that the proposed strategy effectively resolves the hidden and exposed node problems associated with traditional LBT strategies. Furthermore, we consider the sensitivity of mmWave to obstacles and analyze the effects of these obstacles on the spectrum-sharing sensing scheme. We examine the unlicensed spectrum access probability and network throughput under blockage scenarios. Simulation results indicate that although obstacles can attenuate the signal, they positively impact unlicensed spectrum sensing. The presence of obstacles can increase spectrum access probability by about 60% and improve system capacity by about 70%.

A genetic algorithm method for improving suboptimal sensor arrangements in coverage and connectivity problems

This paper is concerned with the solution of a Coverage and Connectivity Problem (CCP). The proposed method performs the placement of sensors forming a connected Wireless Sensor Network (WSN) to fully cover a Field of Interest (FoI). Full coverage is attained despite the presence of opaque obstacles that impair both sensing and communication. To this end, we leverage set operations involving polygons to tessellate the free space. Moreover, we propose the concept of CR-visibility to assess the total area coverage of a polygon by a sensor. The deployment of the minimal number of sensors to completely cover the FoI is cast into the form of an Integer Linear Program (ILP). Two different formulations of connectivity constraints are appended to the ILP. The first one is necessary and sufficient for connectivity, whereas the second alternative is only sufficient. While the number of inequalities in the former grows combinatorially with the number of sensors, the growth is linear in the latter, rendering it more computationally appealing for real-sized FoI. Lastly, we formulate an unconstrained version of the CCP, which is solved by a Genetic Algorithm (GA) with integer variables. We present a small simulation scenario for initial illustration of the proposed method, and a larger, real-life scenario based on the map of an actual urban setting. The results obtained with the real-life scenario show that: (i) high-quality solutions can be obtained in short computation times by imposing the sufficient constraints for connectivity; (ii) the large number of inequalities associated to the necessary and sufficient constraints render the numerical solution impractical; (iii) by using random initialization, the GA solution of the unconstrained problem requires more sensors than the ILP solution with the sufficient connectivity constraint; (iv) including that ILP solution in the initial population of the GA enables it to find a sensor placement that requires fewer sensors.

An Assistant System For Blind To Avoid Obstacles Using Artificial Intelligence Techniques

This study focuses on developing an assistive system for blind individuals for collision avoidance of obstacles by combining artificial intelligence techniques; Convolutional Neural Networks (CNN), fuzzy logic control (FLC), and genetic algorithms(GA), This integrated system, named the (NFG) Neural Fuzzy Genetic). The proposed system combines artificial intelligence techniques through detecting and tracking objects, measuring the distance between objects and the blind person, and providing movement guidance using three ultrasonic sensors with FLC and optimization GA. The integration of these technologies offers an innovative solution to enhance the mobility and safety of blind individuals.Specifically, object detection and tracking are applied through CNN, with an obstacle detection range of up to 40 meters. The obstacle recognition system is trained on the ResNet50 model, which includes 50 million trained images and more than 1,000 obstacle classifications, resulting in high accuracy in identifying and detecting obstacles. When tested, the accuracy of the trainer model reached 99.9%. FLC is then used to provide motor guidance and help make appropriate decisions in the presence of obstacles, navigate safely and independently, and determine movement directions in obstacle-free paths with the help of three sensors.