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Analyzing the surplus and deficit in the public budget and calculating their gap is one of the most important topics of fiscal policy because of the role of public spending and taxes in facing economic fluctuations, as well as their role in stimulating growth, with the intensification of inflationary pressures in addition to the increasing burdens of external and internal debts. Which would increase the financial burdens on the national economy, and then future generations will bear the greater part of its burdens, which negatively affects their levels of well-being as a result of bearing burdens they did not participate in making their own decisions, Rather, they may not have benefited from it in the first place. The study aims to analyze the reality of the public budget and the role of non-oil revenues in financing the public budget, with an analysis of the reality of public debt (internal and external) and the net impact that public debt has on financing the public budget deficit. The study found that the Iraqi budgets are distributive budgets whose main task is to distribute the Iraqi oil revenues on the traditional exchange doors without leading to a qualitative or structural change in the structure of the Iraqi economy. The actual tax revenues did not exceed at best 12% of public revenues. Although this percentage, with its modesty, is also dependent on oil revenues in a large proportion, the taxes are mostly the result of lung is infect or not. income taxes achieved for their owners as a result of contracts and work with government departments that in turn depend on oil, and despite the reduction in public debt after 2003, but It still constitutes an obstacle to the reconstruction of Iraq

Machine learning models based on sensitive data in the real-world promise advances in areas ranging from medical screening to disease outbreaks, agriculture, industry, defense science, and more. In many applications, learning participant communication rounds benefit from collecting their own private data sets, teaching detailed machine learning models on the real data, and sharing the benefits of using these models. Due to existing privacy and security concerns, most people avoid sensitive data sharing for training. Without each user demonstrating their local data to a central server, Federated Learning allows various parties to train a machine learning algorithm on their shared data jointly. This method of collective privacy learning results in the expense of important communication during training. Most large-scale machine-learning applications require decentralized learning based on data sets generated on various devices and places. Such datasets represent an essential obstacle to decentralized learning, as their diverse contexts contribute to significant differences in the delivery of data across devices and locations. Researchers have proposed several ways to achieve data privacy in Federated Learning systems. However, there are still challenges with homogeneous local data. This research approach is to select nodes (users) to share their data in Federated Learning for independent data-based equilibrium to improve accuracy, reduce training time, and increase convergence. Therefore, this research presents a combined Deep-QReinforcement Learning Ensemble based on Spectral Clustering called DQRE-SCnet to choose a subset of devices in each communication round. Based on the results, it has been displayed that it is possible to decrease the number of communication rounds needed in Federated Learning., Comment: 14 pages, Accepted, Elsevier (Journal of King Saud University - Computer and Information Sciences)

It is important to accurately obtain the motion information of the ego vehicle and surrounding vehicles for autonomous vehicles to plan safe trajectories in complicated traffic environments. In this paper, a LiDAR-based estimation method is developed to simultaneously identify the pose and the velocity information of an ego vehicle and its surrounding moving obstacles. Specifically, a pose estimation network is designed to estimate the poses of both the ego vehicle and the obstacles only with the continuous point clouds obtained by the LiDAR mounted on the ego vehicle. In the network, PointNet++ is utilized as the backbone to extract point-wise features and divide the points into the static part and the moving part. The former is used to estimate the ego vehicle's pose, while the latter is applied for the obstacle pose identification. Then, a reduced-order observer is designed to estimate the velocities, whose convergence is proved with the Lyapunov theory. Finally, both simulation and experiment results are provided to show the effectiveness of the proposed method.

Small-sized unmanned surface vehicles (USV) are coastal water devices with a broad range of applications such as environmental control and surveillance. A crucial capability for autonomous operation is obstacle detection for timely reaction and collision avoidance, which has been recently explored in the context of camera-based visual scene interpretation. Owing to curated datasets, substantial advances in scene interpretation have been made in a related field of unmanned ground vehicles. However, the current maritime datasets do not adequately capture the complexity of real-world USV scenes and the evaluation protocols are not standardised, which makes cross-paper comparison of different methods difficult and hinders the progress. To address these issues, we introduce a new obstacle detection benchmark MODS, which considers two major perception tasks: maritime object detection and the more general maritime obstacle segmentation. We present a new diverse maritime evaluation dataset containing approximately 81k stereo images synchronized with an on-board IMU, with over 60k objects annotated. We propose a new obstacle segmentation performance evaluation protocol that reflects the detection accuracy in a way meaningful for practical USV navigation. Nineteen recent state-of-the-art object detection and obstacle segmentation methods are evaluated using the proposed protocol, creating a benchmark to facilitate development of the field. The proposed dataset, as well as evaluation routines, are made publicly available at vicos.si/resources., 16 pages, 15 figures. The dataset, as well as the proposed evaluation protocols, are published on our website: https://www.vicos.si/resources/

Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can not only provide relevant road condition information to prevent lane departure but also assist vehicle positioning and forehead car detection. However, lane marking detection faces many challenges, including extreme lighting, missing lane markings, and obstacle obstructions. Recently, deep learning-based algorithms draw much attention in intelligent driving society because of their excellent performance. In this paper, we review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Besides, we summarize existing lane-related datasets, evaluation criteria, and common data processing techniques. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm.

An innovative and flexible approach is introduced to address the challenge of self-organizing a group of mobile robots into cubic-spline based patterns without any requirement of control points. Besides the self-organization of mobile robots, the approach incorporates a potential field-based control for obstacle/collision avoidance. This will offer more flexibility to swarm robots to efficiently dealing with many practical situations including smoothly avoiding obstacles during movement, or exploring and covering areas with complex curved patterns. Essentially, this challenge is approached by proposing a formation control model basing on a Smoothed Particle Hydrodynamic estimation technique, which uses special cubic-spline kernel functions applied here to interpolate the density of each robot in the swarm. The moving information is used to weight the distances to the robot’s neighbours available in its field of view. Then an artificial physics mesh is finally built among each robot and its three available neighbours having the smallest weighted distances. Significant results toward emerging cubic-spline patterns are shown with a swarm of foot-bot mobile robots simulated in the ARGoS platform. Analysis results with different metrics are also conducted to assess the performance of the model with different swarm sizes and in the presence of sensory noise as well in the presence of partially faulty robots.

This study presents modular transformable wheels capable of overcoming obstacles. They can be used for indoor service robots or service platforms such as wheelchairs and baby carriages, which often move up and down on thresholds or on sidewalks. The structure of the wheel and transformation mechanism are introduced. The wheel has two built-in brushless DC (BLDC) motors. The BLDC motor is remodeled to add the gear box inside. These motors create a two-degree-of-freedom (2-DOF) transformation of the wheel. The 2-DOF mechanism is achieved by a 7-bar linkage with six rotational joints and one prismatic joint. A platform using the presented modular transformable wheels was used to experimentally verify the ability to climb up and down a staircase.

Smart wheelchairs (SW) technology is one of the solutions to help disabled people who do not have a hand or people to help them. Apart from able to move on its own, a smart wheelchair needs to be safe to use. One of the ways to increase SW safety is the ability to detect obstacles. In this study, we tried to create obstacle detection that can classify the stairs descent and floor based on image processing. To achieve our purpose, Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to increase image contrast. After that, Gray Level Co-occurrence Matrix (GLCM) is used to extract features from the image. Finally, Back Propagation Neural Network (BPNN) is used to classify the image. Based on the test result, BPNN achieves results with 95% Accuracy, 95% Sensitivity, 95% Specificity with an average computation time of 0.0035 s.

Introduction. At the present development stage of the high achievement sport, global transformations are taking place all over the world, which have influenced the intensification of competitive practices and the introduction of innovative technologies in the process of training elite athletes. Unfortunately, both external factors and internal political and economic problems in Ukraine become a serious obstacle to the effective competitive activity of national teams at major international sports competitions, namely: the socio-economic situation in the country calls into question the ability to prepare quality reserve, children's and youth sports suffer as a result of the financial and economic crisis; Insufficient level of logistics and the possibility of migration of the best players and coaches abroad, negatively affects the level of performance of the national team in the international arena. The planning of preparation of highly qualified athletes for the main international competitions should take into account the peculiarities of tournament loads in team and individual sports. Why is it necessary to determine the main factors influencing the quality and effectiveness of the performances of the best athletes and teams in sports games. Aim of our research is to analyze the effectiveness of competitive activities of highly qualified athletes in sports games. Material and methods: analysis of planning and competition documents and generalization of special literature, method of expert assessments, pedagogical observation, pedagogical experiment, psychophysiological methods, methods of mathematical statistics. Conclusions: the implementation of effective performance of athletes of Ukrainian national teams in sports on the international arena requires optimization of the process of sports training based on individualization with the parallel use of technical-tactical, psychophysiological models of training of leading players in the world combined with modern capabilities.

Driving obstacle detection has great importance in automatic driving safety. However, the deterioration of image quality in the foggy environment brings many difficulties to the detection and even endangers driving safety. Therefore, in this article, a feature fusion method is designed to improve the performance of the camera sensor in driving obstacle detection under foggy weather. The difference in image features of the same traffic scene on sunny and foggy days is compared, and the reason for the decrease in detection performance of the camera sensor in foggy environments is found. The foggy image dataset is constructed, and the feature fusion of the detection object in foggy and sunny environments is realized through YOLOv3. Compared with the regular trained model, the method in this article improves 30% in mean average precision (mAP), 40% in recall, and 22% in F1-Score of the object detection performance in foggy images. Unlike the image defogging method, foggy images can be directly used for detection and have higher detection accuracy and time-efficient. This is of great significance for improving driving obstacle detection in adverse weather, which is important to ensure driving safety.

Background and Purpose. Obstacle crossing is compromised following stroke. The purpose of this study was to quantify modifications during obstacle clearance following stroke. Subjects. Twelve subjects with stroke and 12 subjects without stroke participated in the study. Methods. Kinematic variables were measured while participants crossed a 4-cm-high obstacle. Subjects with stroke walked at a self-selected speed; subjects without stroke walked at a comparable speed and at a self-selected speed. Results. Several modifications were observed following stroke with both groups walking at self-selected speeds. The affected lead limb was positioned closer to the obstacle before crossing. Affected trail-limb clearance over the obstacle was reduced. Both affected and unaffected lead and trail limbs landed closer to the obstacle after clearance. Swing time was increased in the affected lead limb after obstacle clearance. Fewer modifications were detected at matched walking speed; the trail limb still landed closer to the obstacle. Discussion and Conclusion. Modifications during obstacle crossing following stroke may be partly related to walking speed. The findings raise issues of safety because people with stroke demonstrated reduced clearance of a 4-cm obstacle and limb placement closer to the obstacle after clearance.

The limited land in the city of Yogyakarta is often an obstacle in the arrangement and utilization of urban space. This requires a more comprehensive spatial planning involving all stakeholders. Considering this, the Yogyakarta City government developed the Gandeng Gendong program as a means of collaboration with five elements (city, corporation, campus, village, and community). As part of the 5K element, the UKDW Community Service team was involved in implementing the Gandeng Gendong program through the preparation of the Purbonegaran Kampong master plan in Terban Village. The preparation of this master plan was carried out using a participatory design method that involved residents through interviews and focus group discussions. Data were obtained through observation and interviews, which began with the identification of the local potential of Kampong Purbonegaran as a village of culture, v@egetables, and herbs. This activity was carried out for 2 years (2020-2021) with partners namely Purbonegaran Kampong in the first year and thereafter with the Purbonegaran Keluarga Berkualitas (KB) Working Group whose task was to develop welfare in the social and economic fields. This master plan was prepared with a strategy of mapping the potential, problems, strengths, and weaknesses of Purbonegaran Kampong. Thus, the resulting master plan is expected to be right on target. The results of this community service activity are in the form of a master plan containing facilities and infrastructure development programs that are predicted to have an impact on people's lives, including Strawberry Lorong, Belik Kids Learning Studio, Ecology Park, Bamboo Lorong and meeting points, as well as portable kiosks used for entrepreneurship of Keluarga Berkualitas (KB) Purbonegaran.

The spacecraft relative motion trajectory planning is one of the enabling techniques for autonomous proximity operations, especially in the increasingly complicated mission environments. Most traditional trajectory planning methods focus on improving the performance criteria in the deterministic conditions, whereas various uncertain elements in practice would significantly degrade the trajectory performance. Considering the uncertainties underlying the collision avoidance constraints, this paper suggests a model predictive control based online trajectory planning framework in which the obstacle information in higher-precision would be consistently updated by the onboard sensor. To improve the computational efficiency of the online planning framework, the rotating hyperplane (RH) technique is utilized to transform the nonlinear ellipsoidal keep-out zone constraints into convex formulations. And the concept of rotation window is introduced to eliminate the unexpected mismatch between the spacecraft motion and hyperplane rotation in the conventional RH method, which in sequence improves the RH method’s capability for multiple obstacle avoidance problem. Moreover, a three-dimensional (3-D) extension strategy is proposed to simplify the computation procedure when applying the RH method for a 3-D collision avoidance problem. Numerical simulations are carried out to validate the performance of the proposed online trajectory planning framework in addressing the uncertain collision avoidance constraints.

O propósito deste artigo é mostrar como se deu a passagem das intenções iniciais à criação literária digital e-Imigraçõesapresentando-a como um work in progresse não como ela pode ser lida já pronta (em https://www.editorainverso.com.br/livro-digital[1]). De certo modo, isso pode ser entendido como a tradução dinâmica de um esquema inicial abstrato em uma criação que aos poucos vai-se tornando concreta.. Quando de sua construção, a primeira dificuldade com que deparamos foi o fato de ela pôr em diálogo três diferentes linguagens (a das histórias-em-quadrinhos, a da poesia verbal e a da programação computacional). Isso representou a ameaça constante de criarmos não uma, mas três obras distintas que não dialogariam minimamente entre si. Em termos de método de criação, esse é o primeiro obstáculo a ser enfrentado, é a primeira ação a ser encetada em qualquer criação literária digital. Para discutir essas questões que surgiram ao longo da criação de e-Imigrações, neste artigo exploramos a documentação técnica e criativa por trás da obra. Com isso, tornamos públicas as fontes primárias, por assim dizer, que estão na gênese de nossa criação. [1]É fundamental que se navegue pela obra, antes de ler este artigo.

One of the most challenging tasks for autonomous robots is avoiding unexpected obstacles during their path following operation. Follow the gap method (FGM) is one of the most popular obstacle avoidance algorithms that recursively guides the robot to the goal state by considering the angle to the goal point and the distance to the closest obstacles. It selects the largest gap around the robot, where the gap angle is calculated by the vector to the midpoint of the largest gap. In this paper, a novel obstacle avoidance procedure is developed and applied to a real fully autonomous wheelchair. This proposed algorithm improves the FGM’s travel safety and brings a new solution to the obstacle avoidance task. In the proposed algorithm, the largest gap is selected based on gap width. Moreover, the avoidance angle (similar to the gap center angle of FGM) is calculated considering the locus of the equidistant points from obstacles that create obstacle circles. Monte Carlo simulations are used to test the proposed algorithm, and according to the results, the new procedure guides the robot to safer trajectories compared with classical FGM. The real experimental test results are in parallel to the simulations and show the real-time performance of the proposed approach.

According to the requirements of the reconnaissance robot for the ability to adapt to a complex environment and the in-depth study of the obstacle climbing mechanisms, a planetary wheel-leg-combined mechanism capable of adapting to complex terrains is proposed. According to the proposed planetary wheel-leg-combined mechanism, the land part of the air-ground amphibious reconnaissance robot is designed. Considering the obstacle and fast marching performance, four groups of combined wheel-leg mechanisms are adopted in the land bank. Under the action of three kinds of obstacles, the stability and the movement ability of the robot are analyzed by using the static method. The parameter model of the reconnaissance robot is built by a virtual prototype dynamics software MSC.ADMAS. The kinematic characteristic curves of each component and the whole prototype are obtained, which provides a theoretical basis for the design and numerical calculation of the robot structure. Finally, the climbing ability tests of the reconnaissance robot prototype verify the reliability and practicability of the body structure of the reconnaissance robot.

PurposeOwing to multiple superiorities to traditional counterparts, prefabricated construction (PC) has gained increasing attention worldwide. The development of PC projects reflects the effects of both policy supervision and PC practice, which aids the government in reasonably identifying the key issues of PC's promotion and rationally improving the policy deployment. However, existing studies fail to address this aspect, especially lacking quantitative exploration. This study explores the micro mechanism of PC's promotion, from the perspective of developing PC projects.Design/methodology/approachA tripartite evolutionary game model based on prospect theory of the government, developers and contractors is constructed. After rigorous theoretical deduction, this study adopts Changchun in China as a case city and collects the data using the Delphi technique, policy documents and literature analysis.FindingsResults indicate that contractors are generally willing to implement PC projects and the government chooses to actively supervise PC's promotion. The negative investment behavior of developers is the main obstacle to promote PC in Changchun currently.Practical implicationsThe conclusions are applicable to other comparable regions. This study is of value to promote PC with high efficiency and effect.Originality/valueThe tripartite evolutionary game model based on prospect theory proposed in this study is conducive to reveal the essence of PC's promotion. This is an important breakthrough in extant studies, with a broad applicability in the PC domain beyond China.

According to the inherent nature of the fluid that can naturally avoid obstacles, a path planning method for active collision avoidance of autonomous vehicles is presented based on the virtual flow field. Firstly, the mathematical model of the virtual flow field on the road is established by using the theory of hydrodynamics. Then a fifth degree polynomial curve is adopted to construct the virtual hazard area of the obstacle vehicle to prevent the fluid into this area, and it can be easily resized by adjusting the parameters of the lateral and longitudinal safety distance. Finally, Computational Fluid Dynamics (CFD) simulations are performed to quantitative predict the dynamic behavior of the ego vehicle on the straight or curved road and the desired path for active collision avoidance can be determined based on the calculation result of the flow field. The simulation results show that the proposed path planning method takes into account the dynamic characteristics and kinematic constraints of the vehicle, and ensures that the vehicle doesn’t collide with the dynamic and static obstacles on the road.

This paper investigates the multi-sensor fusion perceptual target recognition based on machine learning visual feature extraction. On the basis of the efficient neural network model (Enet), an improved efficient semantic segmentation network model (Enet-CRF) is designed according to the requirements of the research content. This network model merges a CRF-RNN back-end optimization network to the original Enet, and further improves the classification performance of the network by adding constraints on the position relationship between image pixels and RGB information. The experiments indicate that the designed Enet-CRF has a certain improvement in obstacle classification performance, especially for small-scale obstacles such as pedestrians and bicycles. By deep fusion of LiDAR and vision sensors at the data level and higher-order features, this method adds high-precision radar information to the original Enet-CRF network model, thereby further enhancing the accuracy of obstacle classification and achieving an obstacle recognition method. This approach can not only accurately classify obstacles, but also attain accurate spatial information of obstacles, with good real-time performance.

Global reliance on fossil fuels presents a significant obstacle to climate change mitigation. While numerous countries have committed to phasing out their reliance on fossil fuels, many have not fo...

History instructs us that electrical power can be decarbonized in less than a dozen years with combinations of renewable and nuclear energy, but exaggerated fears of the latter have made it too cos...

Autonomous spacecraft proximity technology is vital for current and future on-orbit service missions, especially to solve the difficult problem of ultra-close-range safe proximity to complex-shaped spacecraft. This paper designs a novel Gaussian mixture model (GMM) based artificial potential function (APF) to describe the movement constraint, which is generated by the complex shape of the target spacecraft. Next, a novel GMM-FTC controller is designed by combining fixed-time control (FTC) with GMM-APF. The proposed GMM-FTC not only retains the advantages of FTC method, whose stabilization time is independent of the initial state, but also solves the collision avoidance in the presence of complex shape obstacle. Finally, numerical simulation verifies the effectiveness of the developed method.

Existing obstacle avoidance systems do not consider the situation of hollowed-out obstacles. Therefore, in this paper, we design an assistive cane with visual odometry based on these requirements of the blind to aid them in attaining safe indoor navigation. The proposed device is portable, compact and adaptable. The assistive cane with visual odometry can be used not only to directly detect obstacles, but also to draw the attention of people around, providing multiple protection for the blind. In order to optimize the device parameters, we select the height of the camera and the speed of the voice broadcast as factors to design a complete factorial design, aiming to explore the parameter level that can make the passing time shortest and the success rate of obstacle avoidance highest. We invited two visually impaired people and two blindfolded students with normal vision as the experimental subjects. Each subject was required to conduct the experiments under nine combinations of device parameters. The subjects tried to avoid obstacles according to the voice prompts. The passing time and the success rate of obstacle avoidance were recorded. The experimental results show that the broadcast speed causes the significant effect to the success rate and both the two factors affect the passing time. In addition, we discuss the results of different subjects’ response surfaces and perceptual patterns and get the conclusion that different visual perceptual patterns, namely route representation and two different survey representations, have a certain impact on the setting of the optimal parameter level and the optimal passing time. The congenitally blind subject with route representation has the shortest optimal passing time in an unfamiliar environment, and the camera height should be set at the center of the hollowed-out obstacle, while the results are somewhat different for the acquired visual impairment subjects and the blindfolded normal vision subjects with survey representation. The camera height should be set between the center and the upper position of the obstacle. In summary, the camera height should be set at the center of the hollowed-out barrier and the broadcast speed should be set at the highest level. At the same time, the device parameters should be adjusted appropriately according to the visual perception pattern of users. Further researches are needed to analyze the perceptual patterns in depth.

Resumen La categoría de participación permite observar empíricamente cómo las interacciones sitúan a las personas de maneras específicas en el marco de prácticas sociales concretas. El objetivo de este trabajo es caracterizar el modo en que se organiza la participación en las reuniones de un equipo interdisciplinario de salud en un hospital de Buenos Aires, Argentina. Para ello, analizamos las interacciones orales atendiendo a la manera en que se gestionan los turnos de habla y se interpretan las instancias de habla simultánea en el contexto de dos actividades vinculadas con la toma de decisiones y la resolución de obstáculos en la interacción: una propuesta de escritura y una secuencia de reparación. A través del análisis, definimos la participación conjunta como una forma de participación en la que se prioriza la construcción colectiva por sobre las voces individuales y en la que el habla simultánea tiene naturaleza cooperativa, no disruptiva. Asimismo, identificamos los efectos que produce en las reuniones, orientados a la construcción del consenso, el establecimiento de la continuidad temática y el mantenimiento de las relaciones interpersonales. A la luz de estos resultados, observamos que la participación conjunta tiene un rol central en el proceso de construcción y mantenimiento de una identidad grupal (ser un equipo), reforzando la inscripción de los profesionales en ella.

This study reviews obstacle detection technologies in vegetation for autonomous vehicles or robots. Autonomous vehicles used in agriculture and as lawn mowers face many environmental obstacles that are difficult to recognize for the vehicle sensor. This review provides information on choosing appropriate sensors to detect obstacles through vegetation, based on experiments carried out in different agricultural fields. The experimental setup from the literature consists of sensors placed in front of obstacles, including a thermal camera; red, green, blue (RGB) camera; 360° camera; light detection and ranging (LiDAR); and radar. These sensors were used either in combination or single-handedly on agricultural vehicles to detect objects hidden inside the agricultural field. The thermal camera successfully detected hidden objects, such as barrels, human mannequins, and humans, as did LiDAR in one experiment. The RGB camera and stereo camera were less efficient at detecting hidden objects compared with protruding objects. Radar detects hidden objects easily but lacks resolution. Hyperspectral sensing systems can identify and classify objects, but they consume a lot of storage. To obtain clearer and more robust data of hidden objects in vegetation and extreme weather conditions, further experiments should be performed for various climatic conditions combining active and passive sensors.

This study explores the effect of geopolitical risk on renewable energy deployment, assuming that geopolitical uncertainty stimulates nations to be independent and rely on their renewable energy sources in order to reduce the geopolitical risk concomitant to fossil fuel inflows. The current paper's data sample covers 10 net crude oil importer countries during the period 1985–2017. It employs panel cointegration analysis and estimate an autoregressive distributed lag model. The results show that geopolitical risk has a significant and positive effect on renewable energy diffusion. Therefore, this work determines that it is an incentive, not an obstacle, to renewable energy deployment. The present study's policy implication is that renewable energy development is expected to increase if the geopolitical risk rises, all else being equal.

In this paper, we study the accuracy and robustness of the computational time reversal approach based on the explicit finite element method for application in nondestructive testing in solids. The main goal of this paper is to find a methodology for correct and accurate reconstruction of the original source time history. For numerical modeling of frontal (forward) and reverse (backward) problems of elastic wave propagation, we use the finite element method and explicit time integration with the lumped mass matrix. The suggested methodology is applicable in each finite element open source or commercial software. A special attention is paid to prescription of boundary conditions/loading for the reverse problem for accurate reconstruction of time history of the original source. For evaluation of the reconstruction quality, we suggest certain cost functions. Based on several numerical tests, we show effects of prescription of boundary conditions/loading in time reversal, effect of mesh size and time step size, an unknown obstacle, a number of sources, and environmental disturbance (noise) on the correctness of reconstruction of the original source.

– Indonesian Army has the main task of protecting against enemy attacks, which include forest battles as well as urban battles. The obstacle faced today is that in the process of urban warfare operations, infiltration and hostage rescue in buildings are still less efficient and optimal. The robot is designed with a system that can identify friends and foes using a Night Vision camera and the Pattern Recognition method. Pattern recognition is a symbolic grouping automatically that is done by a computer to find out objects or patterns. The results of the Night vision camera test are able to detect human objects with a maximum distance of 6 meters. This night vision scope has a fairly large accuracy rate of 83%. And light intensity has an influence on the identification process because if the light intensity is more than 200 lux then the system is not able to identify the object.

Electronic mobility aid, transforms visual information to another sensory modality, has proved to be useful for visually impaired people to commute confidently and independently. With recent developments in technology, more visual information can be provided that can assist the user in the better way to avoid obstacles. The paper is focused on portable mobility aid prototype based on stereo imaging that can help user avoid collision with the obstacles. The algorithm is based on segmentation of disparity image to detect obstacles in each segment and identify probable path free of obstacles. The information about the free path is conveyed to the user with the help of two vibrotactile sensors. The prototype was tried on visually impaired users. The experiments were conducted in terms of detection of obstacles, avoidance of obstacles and walking speed of visually impaired user in closed area with different number of obstacles. We conclude that mobility aid prototype is potentially effective for visually impaired users.

Education problems in remote underprivileged regions in China have long been a prominent obstacle that inhibit the development of education equality. Among all solutions, internet education has the capacity to offset against the deficiencies of unequal distribution of educational resources. Analyzing the success of online education of Lushi County in Henan offers an opportunity of establishing a relatively more equal education system.

This Research is motivated by the lack of communication between childrn and parents which has an impact on the occurrence of obstacles to the moral development of the child. This phenomenon is the effect of the advancement of mass media and the lack of attention from parents to their children. In response to this, it is necessary to conduct a study to determine the inhibiting factors of parent-child communication. thus, a solution to the existing problem will be obtained. In order to achieve these objectives in this study used qualitative-descriptive research methods with literature studies. The results of this study obtained data and fact about the negative effects caused by the mass media on the communcation relationship between children and parents as a result of the lack of parental attention to children in accessing mass media

The outbreak of the Corona virus has rapidly escalated globally as a Pandemic. Due to the characteristic dental setup & procedures the risk of cross infection is sky high not only to the Dental health care personnel but also to the patients. During this time utilizing digital resources to assist patients can be of great advantage. Doing less invasive procedures with more protection and screening can be the finest way to battle the situation. This pandemic situation provides an unprecedented opportunity for the dental community to shift the practice towards the use of Teledentistry. Teledentistry may overcome the obstacle in providing sufficient oral health services comfortably during the current pandemic situation. Although the world of Dentistry has many challenges the main aim is to provide value-based dental care and exploit the tremendous potential of Telecommunication services & other virtual and digital alternatives to confront the challenges that are faced due to infection control, lack of awareness etc. Keywords: Artificial intelligence,COVID-19, Dentistry, Pandemic, Teledentistry.

Obstacle avoidance is a major challenge during independent mobility for blind or visually impaired (BVI) people. Typically, BVI people can only perceive obstacles at a short distance (about 1 m, in case they are using the white cane), and some obstacles are hard to detect (e.g . , those elevated from the ground), or should not be hit by the white cane (e.g . , a standing person). A solution to these problems can be found in recent computer-vision techniques that can run on mobile and wearable devices to detect obstacles at a distance. However, in addition to detecting obstacles, it is also necessary to convey information about them in real time. This contribution presents WatchOut , a sonification technique for conveying real-time information about the main properties of an obstacle to a BVI person, who can then use this additional feedback to safely navigate in the environment. WatchOut was designed with a user-centered approach, involving four iterations of online listening tests with BVI participants in order to define, improve and evaluate the sonification technique, eventually obtaining an almost perfect recognition accuracy. WatchOut was also implemented and tested as a module of a mobile app that detects obstacles using state-of-the-art computer vision technology. Results show that the system is considered usable and can guide the users to avoid more than 85% of the obstacles.

In guiding the energy transition efforts towards renewable energy sources, 3D city models were shown to be useful tools when assessing the annual solar energy generation potential of urban landscapes. However, the simplified roof geometry included in these 3D city models and the lack of additional semantic information about the buildings’ roof often yield less accurate solar potential evaluations than desirable. In this paper we propose three different methods to infer and store additional information into 3D city models, namely on physical obstacles present on the roof and existing solar panels. Both can be used to increase the accuracy of roof solar panel retrofit potential. These methods are developed and tested on the open datasets available in the Netherlands, specifically AHN3 lidar point-cloud and PDOK aerial photography. However, we believe they can be adapted to different environments as well, based on the available datasets and their precision locally available.

The existence of a pandemic has an impact on the education sector, namely teaching and learning activities are carried out by educators and students, so that both parties must be able to adapt to technology-based learning, besides that the school and the learning environment contribute to the adaptation process. The purpose of this study is to analyze the adaptation of technology-based education in the face of the COVID-19 pandemic. The following study uses a method in the form of descriptive content analysis study. The analysis was carried out through various scientific articles related to the adaptation of technology-based education in the face of the Covid-19 pandemic. Scientific articles are obtained from national journals and various other similar references. The results of the study show the adaptation of technology-based education in dealing with the Covid-19 pandemic in various forms, including a) HR readiness: Educators provide material through online media such as Google Classroom, Google Meet, Zoom, Google Form, YouTube, students receive material using similar media used given by educators. b). Readiness of learning materials: the material is packaged in an attractive, easy-to-understand way in various forms such as power points, learning videos/practicums. c). The readiness of the relevant learning model is a student-centered learning model, student-centered learning with material packaged by the teacher in the form of videos, students develop it using various sources, then linking it between problems and theory. d). Readiness of IT-based learning facilities: educators, students, schools, learning environments require online-based learning tools with stable internet networks, laptops, mobile phones, internet quotas. The main obstacle in the adaptation process of technology-based education is the readiness of facilities and infrastructure in the form of limited laptop devices, mobile phones, internet quotas. This requires the cooperation of various parties involved in the world of education as an adaptation of technology-based education in the face of the Covid-19 Pandemic.

Literature reviews over five decades have reported the paucity of examples of OR methods being routinely used to support decision-making in health and social care. This paper presents insights from an independent evaluation of a project intended to overcome some of the barriers to implementation by establishing a “community of practice” in Kent (England). The project itself was undertaken by practitioners, and had two main aims: providing training in system dynamics modelling to analysts, and making senior managers aware of the benefits of modelling. The findings largely confirmed previous studies, but also raised issues about style of training delivery and selection of problems to be modelled. Project leaders fully understood the barriers to embedding OR modelling skills, and made considerable efforts to avoid them, but nevertheless the main barrier, pressures on people’s time, remained an obstacle. The paper concludes with general reflections and advice.

The purpose of this article is to highlight how technologies, which have for centuries revolutionalized healthcare, have conversely increased health disparities among disadvantaged communities in South Africa during coronavirus disease (COVID-19). A hard lockdown imposed on the country in March 2020, as an initial measure of containment of the novel virus, became unfeasible due to detrimental, mostly economic effects on the economy. From 1 May, the government eased the lockdown restrictions and shifted the emphasis from containment to behaviour change measures using communication strategies that promoted adherence. Yet, even within disadvantaged communities, there have been differences in health-related access with differing levels of disadvantage. Secondary data were analysed from a cross-sectional quantitative survey conducted in ten disadvantaged communities in the Northern Cape and Gauteng Provinces involving 1742 participants. Results show that the lack of digital access to information created an obstacle to adherence to some recommended COVID-19-related behaviour change measures. Disparities existed between the two provinces’ disadvantaged communities based on an urban versus a rural province. The study emphasized the need to assess the extent of underlying obstacles to health equity and achievement of healthcare goals propagated by an interface between healthcare and socio-economic factors. Breakthroughs in healthcare, facilitated by virtual modes of communication may be undermined by a digital divide exacerbating disparities in disadvantaged communities.

Course estimation is a key component for the development of autonomous navigation systems for robots. While state-of-the-art methods widely use visual-based algorithms, it is worth noting that most fail to deal with the complexity of the real world. They often require obstacles to be highly textured to improve the overall performance, particularly when the obstacle is located within the focus of expansion (FOE) where the optic flow (OF) is almost null. This study proposes the FAst ITerative Half-plane (FAITH) method to determine the course of a micro air vehicle (MAV). This is achieved by means of an event-based camera, along with a fast RANSAC-based algorithm that uses event-based OF to determine the FOE. The performance is validated by means of a benchmark on a simulated environment and then tested on a dataset collected for indoor obstacle avoidance. Our results show that the computational efficiency outperforms state-of-the-art methods while keeping a high level of accuracy. This has been further demonstrated onboard an MAV equipped with an event-based camera, thus showing that the FAITH algorithm can be achieved online and is suitable for autonomous obstacle avoidance and navigation onboard MAVs.

Localization is necessary for applications such as warning systems, where the sensed data along with the location of occurrence is essential for decision making in Wireless Sensor Networks (WSNs). Equipping each sensor node with a global positioning system (GPS) unit is infeasible, as it increases both the power consumption and cost of the deployed network. A more practical approach is to employ a single or a few GPS-equipped mobile anchor nodes (MANs). These MANs localize the unknown sensor nodes by broadcasting their current locations periodically while moving in the area of interest. In a realistic environment, obstacles may be present in the deployment area that obstruct the radio connectivity between unknown sensors and the MANs. In this paper, we propose a mechanism to handle irregular obstacles in the path of various existing static trajectories. Also, we have considered an accurate and reliable channel model to provide a realistic evaluation of the proposed method. The results are compared on the basis of four different localization techniques, namely, accuracy priority trilateration (APT), time priority trilateration (TPT), weighted centroid localization (WCL) and weight-compensated weighted centroid localization (WCWCL). Experimental and simulation results show that the proposed method effectively handles regular and irregular obstacles in the deployed environment.

To solve the problems of difficult control law design, poor portability, and poor stability of traditional multi-agent formation obstacle avoidance algorithms, a multi-agent formation obstacle avoidance method based on deep reinforcement learning (DRL) is proposed. This method combines the perception ability of convolutional neural networks (CNNs) with the decision-making ability of reinforcement learning in a general form and realizes direct output control from the visual perception input of the environment to the action through an end-to-end learning method. The multi-agent system (MAS) model of the follow-leader formation method was designed with the wheelbarrow as the control object. An improved deep Q netwrok (DQN) algorithm (we improved its discount factor and learning efficiency and designed a reward value function that considers the distance relationship between the agent and the obstacle and the coordination factor between the multi-agents) was designed to achieve obstacle avoidance and collision avoidance in the process of multi-agent formation into the desired formation. The simulation results show that the proposed method achieves the expected goal of multi-agent formation obstacle avoidance and has stronger portability compared with the traditional algorithm.

3D motion tracking is a challenging task when both the tracked object and the observer are moving. In this paper, we present a multi-behavioural social force-based particle filter to track a group of moving humans from a moving robot using a limited field-of-view monocular camera. The application is a robotic guide and while moving, the robot often loses visibility of one or more people in the group, who must still be tracked. As an example, due to limited space, when the robot takes a sharp turn to avoid an obstacle or circumvent a corner, the visibility of the people at the rear is lost for some time. Therefore, several human social behavioural aspects have been implemented to predict the human’s motion in a group. The model accounts for attraction and repulsion between the people of the group and those with the robot, to maintain a comfortable social distance with each other at equilibrium. Additionally, when any person leaves the group then the track is deleted and after joining the track is automatically re-initialized. In the literature, the time of invisibility is a criterion to detect a person who has left the system, which however cannot be used here since the invisibility may be due to a limited field of view or the robot making a sharp turn to avoid an obstacle or circumventing a corner. Social heuristics are used to accurately detect people leaving the robotic system. The tracked trajectory is compared with ground truth and our system gives a very less error when compared with several baseline approaches. False positives are reduced, and the accuracy also increased with our proposed model as compared to other baseline methods. This method has been tested on several scenarios to ensure its validity.

With the recent changes in this world due to the pandemic of COVID-19 came the need to change in technology with medical environments. There were few robotic surgeries done in medical field, but the pandemic has put the Doctors and health care workers at risk. So there came a need for rapid change in medical environment to replace man with robots with the help of AI. In this paper a AGV also called as Automatic Guided Vehicle is designed for the benefit of health community. It can also be called as Automated Cart. The chances of health care worker getting affected from the patient in this COVID-19 is more due to the behavior of the novel Corona Virus Spread. Hence this Automated cart is designed in this paper which moves near the patient’s beds delivering medicines whenever needed in time and also collects waste from patients’ bed and returns to the necessary point. It is a line follower automated cart robot it makes use of certain sensors like infrared sensors and ultrasonic sensors. These sensors are used for route mapping and obstacle detection. This robot at the time of giving medicine to the patients’ bed and collecting waste, it also checks the body temperature and pulse rate of the patient and sends information to the doctor via internet. The adaptability of this robot with the patients depends on the preprogram done. A microcontroller is made use for this purpose. This automated cart can be designed and implemented with low cost and the risk of Doctors, health care workers is reduced.

This study attempts to explore the factors that may lead to the choice behind sophisticated management accounting practices (SMAP) in an emerging economy, Bangladesh. A semi-structured questionnaire has been developed to capture the market data and different descriptive and inferential statistical tools have been used to test relevant hypotheses. The findings of the study are helpful for the management accounting practitioners, academics, and researchers to understand the current state of management accounting practices in an emerging market. In addition, the study brings an extension to existing literature by exploring potential causal relationship between sophistication in applying management accounting tools and satisfaction of management accounting practitioners. This study confirms that there is a missing link between practitioners’ satisfaction and SMAP. It gives a signal to the market that the critical decision-making process is not supported by tactical exercises and market lacks professionalism greatly which may act as an obstacle to develop a competitive business environment.

The pandemic Covid-19 has made major changes in almost all aspects of human life, one of which is the education system. Changes in the education system are forced to make it difficult for students to take courageous learning and accept learning materials, especially students at an early age, because learning that is usually done conventionally becomes learning that is free to change, with its implementation inseparable from the role of teachers and technology. This research aims to examine the obstacles and obstacles faced by teachers in carrying out bold learning after Covid-19. The method in this study uses a qualitative method with exploratory characteristics. The results and conclusions in this paper indicate that the obstacles and obstacles when learning courage is that the teacher is not used to it and does not have interesting learning materials to carry out bold learning. The obstacle is the limited information and government, regarding the provision of assistance for school health service facilities.

The hunter and gatherer approach copes with the problem of dynamic multi-robot task allocation, where tasks are unknowingly distributed over an environment. This approach employs two complementary teams of agents: one agile in exploring (hunters) and another dexterous in completing (gatherers) the tasks. Although this approach has been studied from the task planning point of view in our previous works, the multi-robot exploration and coordination aspects of the problem remain uninvestigated. This paper proposes a multi-robot exploration algorithm for hunters based on innovative notions of "expected information gain" to minimize the collective cost of task accomplishments in a distributed manner. Besides, we present a coordination solution between hunters and gatherers by integrating the novel notion of profit margins into the concept of expected information gain. Statistical analysis of extensive simulation results confirms the efficacy of the proposed algorithms compared in different environments with varying levels of obstacles complexities. We also demonstrate that the lack of effective coordination between hunters and gatherers significantly hurts the total effectiveness of the planning, especially in environments containing dense obstacles and confined corridors. Finally, it is statistically proven that the overall workload is distributed equally for each type of agent which ensures that the proposed solution is not biased to a particular agent and all agents behave analogously under similar characteristics., Comment: 17 pages, 11 figures. arXiv admin note: text overlap with arXiv:1912.05748

In this work, we present a framework supported by mobile and web apps and able to propose personalized pedestrian routes that match user mobility profile considering mobility impediments factors. We explain how these later have been defined using a pedestrian-centric approach based on travel experiences as perceived in the field by senior citizens. Through workshops, six main factors that may influence pedestrian route choices were revealed: passability, obstacle in path, surface problem, security, sidewalk width, slope. These categories were used to build digital tools and guide a citizen participatory approach to collect geolocated points of obstacle documented with walkability information (picture, category, impact score, free comment). We also involved citizens to evaluate these information and especially senior referents for validation. Finally we present how we connect these points of obstacle with a pedestrian network based on OpenStreetMap to configure a routing cost function. The framework has been partially deployed in 2020 with limited people due to the pandemic. Nonetheless, we share lessons learned from interaction with citizens in the design of such a framework whose underlying workflow is reproducible. We plan to further assess its relevance and sustainability in the future.

The article is devoted to the work of public charitable organizations and institutions in the Nizhny Novgorod province of the post-reform Russian Empire in various areas of the social sphere. The relevance of the study is due to the development of charitable activities in modern Russia, the need to get acquainted with the historical experience. A review of the activities of voluntary societies, both all-Russian, with branches in Nizhny Novgorod, and, in fact, regional, in the field of social security, health care and public education has been carried out. On the basis of the charters and reports of the companies, their goals, objectives, management structure, sources of funds, forms of assistance to those in need are considered. The author shows the possibilities of social self-organization in the conditions of autocracy, the nature and methods of cooperation between civil society and the authorities. The reasons for the development of charity in the Nizhny Novgorod province in the second half of the XIX century, the motives prompting various segments of the population to engage in it are revealed. The universalism of the work of organizations, in other words unification within the framework of one society of different areas of activity are emphasized in the article. The author comes to the conclusion that the second half of the 19th — the beginning of the 20th century was a period of activity of charitable societies and institutions, whose main obstacle in achieving their goals was a lack of funds.