Orientation Problem Research Articles

Effective use of the tools of probability theory and mathematical statistics in solving the problems of the professional orientation of physicists and mathematicians: an interdisciplinary approach

ABSTRACT The use of methods of mathematical statistics for teaching students carries the task of determining mathematical abilities, formalizing the choice of a professional orientation and the potential choice of solutions. In higher education, the use of the mathematical apparatus allows students to form the ability to multiple choice and assess the conditions of the choice. These include not only the choice of training courses, but also the areas of knowledge in which each student can fulfil themself. For mathematicians, this is especially true, since in most cases mathematics is used to solve purely applied problems. The novelty of the study is determined by the fact that the methods and tools of probability theory cannot be applied exclusively on an individual level. To determine the effectiveness of the methods used, it is necessary to determine the possibilities of group application of the used methodological apparatus and identify areas of assistance to the student in choosing a professional orientation. In terms of the use of ubiquitous distance education, the authors demonstrate the possibility of group work with mathematical support through the use of clusters and network techniques, when each individual is the host of the training programme.

Frequency dependence of reflections on radar landmarks

Purpose. Reducing the dispersion of radar reflections from local objects, with multi-frequency sensing, to solve the problem of orientation by radar reflections from objects. Methodology. Reflections from local objects in the entire frequency range of the radar station (RS) at the same radio engineering position were measured by three independent radars of the same type on different days and at different antenna elevations. The deviation of the radar stations in the position did not exceed 500 meters. Coordinates (azimuth, range) of reflections of several separate local objects were allocated for each radar. The average values of reflections from local objects and their dispersion in the frequency range were calculated. Using various algorithms, individual frequencies were sampled and the reflected signals were averaged at these frequencies. The decrease in the dispersion of the reflected signal from the number of frequencies at which reflections were measured and from the algorithm for selecting these frequencies was investigated. Findings. Averaging the values of reflections from local objects for several frequencies leads to a decrease in dispersion and, as a result, to a more accurate correspondence of the reflected signal level to the geometric size of the local object. The variance decreases most rapidly for a small number of frequencies selected for averaging when selecting frequencies located in an interval of at least 1% relative to each other. Originality. To solve the problem of orientation based on radar reflections from local objects, it is necessary to identify the landmarks selected on a digital terrain model. Due to the fact that local objects (hills) are a collection of many reflectors falling into the allowed volume of the radar, with different levels of reflections and random phases, there may not be radar reflection from a local object at a certain frequency, or it may be very small. In order to unambiguously identify all landmarks, measurements must be carried out at several frequencies. The work has established how many frequencies measurements should be performed at and on what principle these frequencies should be selected. Practical value. The advent of digital terrain models made it possible to solve the problem of terrain orientation by comparing radar reflections from local objects with reflection models based on digital terrain maps. Radar reflection models use mathematical expectations of reflection values, unlike real reflections, which have random deviations in signal levels depending on the operating frequency. Reducing the variance of these deviations increases the accuracy of identifying characteristic local objects (landmarks) used to orient the radar in the absence of data from satellite navigation systems.

The effect of listening to the voice recording of relatives on chest pain, anxiety and depression in patients hospitalized in the coronary intensive care unit: A randomized controlled trial.

After acute myocardial infarction, the prevalence of anxiety and depression is quite high in patients because of severe chest pain, distance from relatives, unfamiliar environment and orientation problems. To assess the effect of listening to the voice recordings of relatives of patients with acute myocardial infarction who were treated in the coronary intensive care unit (ICU) on chest pain, anxiety and depression parameters of the patients. In the study, which was conducted as a randomized controlled trial, voice recordings of the family members of the patients were created and played to the patients through a music pillow. The study was carried out with 60 patients, 30 experimental and 30 control groups. Three tests were applied to the patients 15 min before, and 15 and 30 min after the application. The data of the study were collected using the Patient Introduction Form, Hospital Anxiety Depression Scale, Visual Analogue Scale and Patient Follow-up Form. It was found that there was a significant decrease in the anxiety level of the patients in the intervention group after listening to the audio recording (p < .001, 95% CI: -3.796; -0.070). However, there was no significant difference between the pain and depression scores of the control and intervention groups (p > .05). In the ICUs of patients with acute myocardial infarction, it may be recommended to play audio recordings of their relatives to reduce the severity of anxiety. In the intensive care setting, the use of voice recordings of relatives can be used as an effective, non-pharmacological intervention to reduce anxiety in patients with acute myocardial infarction. This approach may potentially improve overall recovery by reducing patient anxiety in the intensive care setting.

Problems of Value Orientation of Contemporary College Students and Countermeasures

Values, in fact, is the process of cultivating one’s own mind to correctly view money, fame, gain and loss. It serves as the core of human spiritual field and influences human values and behavioural patterns. Contemporary college students are a generation of attention, they have certain knowledge and ability, with the expressive desire of exploration and research. Among the values of college students, values influence all aspects. Therefore, under the condition of socialist market economy constantly tends to improve, the main work of colleges and universities is to deeply understand the current situation of college students’ values, and timely and effectively strengthen the ideological and moral education of college students, in order to cultivate talents and qualified citizens who can be used by the society.

Understanding gait alterations: trunk flexion and its effects on walking neuromechanics.

Evolutionary and functional adaptations of morphology and postural tone of the spine and trunk are intrinsically shaped by the field of gravity in which humans move. Gravity also significantly impacts the timing and levels of neuromuscular activation, particularly in foot-support interactions. During step-to-step transitions, the centre of mass velocity must be redirected from downwards to upwards. When walking upright, this redirection is initiated by the trailing leg, propelling the body forward and upward before foot contact of the leading leg, defined as an anticipated transition. In this study, we investigated the neuromechanical adjustments when walking with a bent posture. Twenty adults walked on an instrumented treadmill at 4 km h-1 under normal (upright) conditions and with varying degrees of anterior trunk flexion (10, 20, 30 and 40deg). We recorded lower-limb kinematics, ground reaction forces under each foot, and the electromyography activity of five lower-limb muscles. Our findings indicate that with increasing trunk flexion, there is a lack of these anticipatory step-to-step transitions, and the leading limb performs the redirection after the ground collision. Surprisingly, attenuating distal extensor muscle activity at the end of stance is one of the main impacts of trunk flexion. Our observations may help us to understand the physiological mechanisms and biomechanical regulations underlying our tendency towards an upright posture, as well as possible motor control disturbances in some diseases associated with trunk orientation problems.

A comparison of recent optimization algorithms for build orientation problems in additive manufacturing

Abstract Build orientation in additive manufacturing technology is a pre-process application that affects many parameters, such as the volume of the support structure, part quality, build time, and cost. Determining the optimum build orientation for one or more objectives for complex parts is an error-prone puzzle. This study evaluates the behavior of cuckoo search algorithm, differential evolution, firefly algorithm, genetic algorithm, gray wolf optimizer, Harris hawks optimization, jaya algorithm, moth flame optimizer, multi-verse optimizer, particle swarm optimization, A Sine cosine algorithm, salp swarm algorithm, and whale optimization algorithm to determine the optimum build orientation of the component to be manufactured additively. The efficiency of these algorithms is evaluated on the build orientation problem of two complex components considering undercut area and build height as objective functions. Thus, the feasibility of these algorithms for real-world additive manufacturing problems is revealed. According to results obtained from the extensive analysis, the cuckoo search algorithm is the best alternative for minimizing undercut area, considering its robustness. However, the required time to solve the problem is as much as almost twice that of other algorithms. The firefly algorithm and particle swarm optimization algorithm are the best alternatives for minimizing build height.

Theoretical and methodological foundations of the organization of sports selection of Spanish football players

In the final match of the 2010 FIFA World Cup, which was held on the fields of the Republic of South Africa (South Africa), the Spanish national team defeated the national team of the Netherlands and became the world champion for the first time in its history. The basis of the Spanish championship team was made up of pupils of the world-famous Barcelona football academy "La Masia". The football academy of the Spanish-Catalan "Barcelona" with the name "La Masia" was founded in 1979, but received real recognition in the 90s of the twentieth century. At that time, the main professional team of the Barcelona football club as a head coach was headed by the Dutchman Johan Cruyff. It was the "Flying Dutchman", as the three-time Ballon d'Or winner was then called the best player in Europe, who introduced a systematic approach and a general educational methodology for educating young people into the activities of the Barcelona academy. The purpose of the study is to determine the features of the organization, conduct and scientific and methodological support of the process of teaching the game and sports selection and game orientation of football players in Spain. The study of the phenomenon of Spanish football as a successful long-term sports project is an important aspect of a systematic approach to solving the problems of sports selection and game orientation of football players in Ukraine.

Online Handwriting Recognition Method with a Non-Inertial Reference Frame Based on the Measurement of Linear Accelerations and Differential Geometry: An Alternative to Quaternions.

This work describes a mathematical model for handwriting devices without a specific reference surface (SRS). The research was carried out on two hypotheses: the first considers possible circular segments that could be made during execution for the reconstruction of the trace, and the second is the combination of lines and circles. The proposed system has no flat reference surface, since the sensor is inside the pencil that describes the trace, not on the surface as in tablets or cell phones. An inertial sensor was used for the measurements, in this case, a commercial Micro-Electro Mechanical sensor of linear acceleration. The tracking device is an IMU sensor and a processing card that allows inertial measurements of the pen during on-the-fly tracing. It is essential to highlight that the system has a non-inertial reference frame. Comparing the two proposed models shows that it is possible to construct shapes from curved lines and that the patterns obtained are similar to what is recognized; this method provides an alternative to quaternion calculus for poorly specified orientation problems.

Temporal orientation: neurotic fixation or resource fullness of personality

The report, using empirical material, reveals the problem of temporal orientation, which can be both resource and fixed. In the study, the respondents were 230 persons (100 men and 130 women) aged 31 to 45 years with higher education, practically healthy, who underwent an online examination at the request of a psychologist. We used the questionnaires "Subjective assessment of temporal orientation," "Experiencing a psychological crisis by a person," "Losses and acquisitions of personal resources." It has been established that a fixed temporary orientation is accompanied by a loss of resources, reducing his adaptive abilities, satisfaction with life, its quality, the results of his activities and activity. The resource time orientation suggests that addressing your past, present and future implies a motivating and increasing impact on the process of accumulating adaptive, adaptive capabilities and on life satisfaction in general.

Branch-cut-and-price algorithms for two routing problems with hotel selection

The Traveling Salesperson Problem with Hotel Selection (TSPHS) and the Orienteering Problem with Hotel Selection (OPHS) are combinatorial optimization problems that arise in various real-world applications, from logistics to tourism. Both problems consider a set of customers and hotels and aim to find a sequence of concatenated trips limited in time. A trip is defined as a sequence of visited customers, starting and ending at hotels, such that any other nodes are customers. The main differences between the problems are the following. The TSPHS assumes that all customers are visited and involves two hierarchical objectives: reducing the number of trips and minimizing the total travel time. On the other hand, in the OPHS, not all customers are required to be visited; rather, each customer is associated with a positive profit, and the objective is to maximize the total profit related to the visited ones. In this paper, we propose exact branch-cut-and-price algorithms for these problems that are coded within the VRPSolver framework. For TSPHS and OPHS, we show that the proposed methods outperform the best exact models from the literature and optimally solve most of the open benchmark instances.

Correction of preferred orientation-induced distortion in cryo-electron microscopy maps.

Reconstruction maps of cryo-electron microscopy (cryo-EM) exhibit distortion when the cryo-EM dataset is incomplete, usually caused by unevenly distributed orientations. Prior efforts had been attempted to address this preferred orientation problem using tilt-collection strategy and modifications to grids or to air-water interfaces. However, these approaches often require time-consuming experiments, and the effect was always protein dependent. Here, we developed a procedure containing removing misaligned particles and an iterative reconstruction method based on signal-to-noise ratio of Fourier component to correct this distortion by recovering missing data using a purely computational algorithm. This procedure called signal-to-noise ratio iterative reconstruction method (SIRM) was applied on incomplete datasets of various proteins to fix distortion in cryo-EM maps and to a more isotropic resolution. In addition, SIRM provides a better reference map for further reconstruction refinements, resulting in an improved alignment, which ultimately improves map quality and benefits model building.

Multi-GPU 3D k-nearest neighbors computation with application to ICP, point cloud smoothing and normals computation

The k-Nearest Neighbors algorithm is a fundamental algorithm that finds applications in many fields like Machine Learning, Computer Graphics, Computer Vision, and others. The algorithm determines the closest points (d-dimensional) of a reference set R according to a query set of points Q under a specific metric (Euclidean, Mahalanobis, Manhattan, etc.). This work focuses on the utilization of multiple Graphical Processing Units for the acceleration of the k-Nearest Neighbors algorithm with large or very large sets of 3D points. With the proposed approach the space of the reference set is divided into a 3D grid which is used to facilitate the search for the nearest neighbors. The search in the grid is performed in a multiresolution manner starting from a high-resolution grid and ending up in a coarse one, thus accounting for point clouds that may have non-uniform sampling and/or outliers. Three important algorithms in reverse engineering are revisited and new multi-GPU versions are proposed based on the introduced KNN algorithm. More specifically, the new multi-GPU approach is applied to the Iterative Closest Point algorithm, to the point cloud smoothing, and to the point cloud normal vectors computation and orientation problem. A series of tests and experiments have been conducted and discussed in the paper showing the merits of the proposed multi-GPU approach.

Novel method of image analysis that combines Radon and Fourier image transformations for the purpose of differentiating between malignant and benign colonic biopsies.

Colorectal cancer is the third most common type of cancer. It develops slowly as a polyp that can turn into a cancerous tumor. This study aimed to develop a decision-making algorithm of microscopic images using texture analysis that is orientation free, to be used for automated classification of normal and neoplastic (malignant or premalignant) colonic biopsies. Forty-nine colonic adenocarcinomas, 41 adenomas and a control group of adjacent normal colonic mucosa were included in the texture analysis. Radon transform followed by the Fast Fourier transform were applied to the images. Subsequently, the gray level co-occurrence matrix (GLCM) transform was applied allowing the extraction of four textural variables (homogeneity, contrast, correlation, and entropy). For classification and prediction of the diagnosis, a statistical multivariate regression model and a neural network (NNET) model were used and compared. The statistical model provided a sensitivity of 71.3% and a specificity of 50% (Area under the ROC curve: 0.67) for classifying the neoplastic and the normal images, respectively. The NNET model was superior to the statistical model and produced a sensitivity of 97.9% and specificity of 88% (Area under the ROC curve: 0.92). To our knowledge, this is the first study that used a combination of Radon, FFT, and GLCM transformations in order to overcome the tissue orientation problem in texture analysis of microscopic images of colonic biopsies. The NNET classifier trained by the extracted textural features proved to be superior to the statistical classifier, thus predicting colonic neoplasia with high accuracy. RESEARCH HIGHLIGHTS: We propose a novel decision-making algorithm of orientation invariant image texture analysis, fast and easily implemented for automated differentiation between benign and neoplastic epithelial tumors of the colon. This method can reduce the turnaround time allowing to prioritize the biopsies during their examination and diagnosis by the pathologist.

MODEL FOR DATA OBJECTS SELECTION BY SEARCH IMAGE FOR INTELLIGENT RECOMMENDER SYSTEMS

The research is conducted to develop and analyze an object filtering model for intelligent recommender systems. The main objective is to solve the problem of orientation in the vast amounts of information accumulated by mankind. The aim of the research is to create an effective tool for systematization and knowledge management, which in turn contributes to the optimization of decision-making processes and interaction with data. The paper focuses on the research and development of an object filtering model for intelligent recommender systems. Within the methodology and research area, the developed model is described in detail and the theoretical and practical aspects of the methodology are analyzed. In this paper, a variant of the problem statement of the research and development of an object filtering model for intelligent recommender systems is presented. In addition, the paper deconstructs the second stage of this problem, emphasizing its importance in the context of achieving system performance. The results of the study are analyzed in detail, highlighting the key points and features of the proposed model. The scope of the study is reviewed, detailing the prospects of applying the results in scientific and practical applications, providing the reader with a deeper understanding of the potential of the proposed model. The object filtering model has a high potential of usefulness for business and manufacturing. This work will be useful for developers and researchers of recommender systems in which users rarely or never interact with the same object.

Spiritual-value guidelines in the educational process: the way to a mature self-concept

The educational process in modern conditions requires not only a high level of professional knowledge and skills from teachers, but also the presence of a mature self - concept based on spiritual and value orientations. This issue becomes particularly relevant today as teachers are seen as agents of change who can influence the future of society. The article under review is devoted to the study of the role of spiritual and value orientations in the formation of a mature self - concept among teachers. In the context of modern social and cultural changes, special attention is paid to developing a harmonious and spiritually mature personality. The analysis of previous studies in this area revealed unresolved aspects, which have become the subject of the current research. The problem of spiritual and value orientations of teachers as a pathway to a mature self - concept is thoroughly explored. For the empirical study, a sample of 420 individuals was formed. These individuals were taking advanced training courses at the Central Institute of Postgraduate Education of the state higher education institution «University of Management of Education» of the National Academy of Sciences of Ukraine in Kyiv. The author's method «The Value of Self - сreation» was utilized in the research, aimed at studying the spiritual and valuable component of the teachers' self - concept. Additionally, the unconditional self - acceptance questionnaire by J. Chamberlain and D. Haaga (2001), adapted by O. Bondarchuk, N. Pinchuk, and O. Prokopenko, was used. The survey was conducted using Google forms. The study's results highlight the importance of spiritual and value orientations in developing a mature self - concept among teachers. The study also points to the need for further scientific exploration in this area to gain a more detailed understanding of the relationship between spiritual values and the professional activities of teachers.

Neuropsychological profile of a patient with multiple sclerosis and psychiatric symptoms that masked and delayed the diagnosis. A case report using teleneuropsychology

Objective: Multiple sclerosis (MS) may include not only severe neurological signs and symptoms, but also cognitive and psychiatric disturbances. When psychiatric symptoms precede or are comorbid with MS, it poses a clinical challenge, because it may lead to a mistaken diagnosis of MS as a psychiatric disorder, delaying proper treatment. We describe the neuropsychological profile of a female patient with MS whose diagnosis was delayed due to neuropsychiatric symptoms. Method: A comprehensive analysis of the medical history and the results of a teleneuropsychological assessment of a 36-year-old Mexican woman with a diagnosis of relapsing–­remitting MS (RRMS) was performed. Results: The patient indicates a long history of psychotic, anxious, and depressive features years before the first neurological symptom that led to MS going unnoticed for several years. Language, attentional, perceptual, motor, and learning skills were found to be preserved. Short-term memory and spatial orientation problems were identified, with decreased processing speed and executive dysfunction, including working memory and planning deficits. Conclusions: The patient has a non-typical presentation of neuropsychological alterations with cognitive and behavioral symptoms that resemble dorsolateral frontal lobe syndrome. This case study highlights the importance of considering MS in differential diagnosis of patients with psychiatric symptoms, even in the absence of obvious neurological signs.

The Role of Social Problem-Solving and Prosocial Behavior in First Aid Willingness among Adolescents in Hungary.

First aid is a key factor in preventing further deterioration in an accident, saving lives, or improving treatment in emergencies. However, the reasons behind the willingness to provide first aid are still unclear. Therefore, this study aimed to investigate the role of social problem-solving and prosocial behavior in the dimension of first aid willingness. Self-administered questionnaires were used to evaluate the dimensions of first aid willingness (first aid willingness for peers, first aid willingness for strangers, knowledge, and negative emotions), social problem solving (positive problem orientation, negative problem orientation, rational problem solving, avoidance style, and impulsivity/carelessness) and prosocial behavior. A total of 497 school-aged students between the ages of 12 and 15 years (201 boys and 296 girls) participated in this study. Our results showed that positive problem orientation (p < 0.05) and rational problem solving (p < 0.001) are significant factors in determining first aid willingness for both peers and strangers. On the other hand, avoidance style orientation (p < 0.05) has a negative influence on the willingness to provide first aid to peers and strangers. Negative problem orientation (p < 0.001) only predicted negative emotions related to emergencies. Furthermore, prosocial behavior (p < 0.001) was more strongly associated with first aid willingness than social problem solving. Our study suggested that improving social competence could be a key factor in increasing first aid provision in real time, which could save lives in emergencies.

Vehicle Geolocalization from Drone Imagery

Abstract. We have developed a robust, novel, and cost-effective method for determining the geolocation of vehicles observed in drone camera footage. Previous studies in this area have relied on platform GPS and camera geometry to estimate the position of objects in drone footage, which we will refer to as object-to-drone location (ODL). The performance of these techniques is degraded with decreasing GPS measurement accuracy and camera orientation problems. Our method overcomes these shortcomings and reliably geolocates objects on the ground. We refer to our approach as object-to-map localization (OML). The proposed technique determines a transformation between drone camera footage and georectified aerial images, for example, from Google Maps. This transformation is then used to calculate the positions of objects captured in the drone camera footage. We provide an ablation study of our method’s configuration parameter, which are: feature extraction methods, key point filtering schemes, and types of transformations. We also conduct experiments with a simulated faulty GPS to demonstrate our method’s robustness to poor estimation of the drone’s position. Our approach requires only a drone with a camera and a low-accuracy estimate of its geoposition, we do not rely on markers or ground control points. As a result, our method can determine the geolocation of vehicles on the ground in an easy-to-set up and costeffective manner, making object geolocalization more accessible to users by decreasing the hardware and software requirements. Our GitHub with code can be found at https://github.com/OSUPCVLab/VehicleGeopositioning

On solving close enough orienteering problems with overlapped neighborhoods

The Close Enough Traveling Salesman Problem (CETSP) is a well-known variant of the classic Traveling Salesman Problem whereby the agent may complete its mission at any point within a target neighborhood. Heuristics based on overlapped neighborhoods, known as Steiner Zones (SZ), have gained attention in addressing CETSPs. While SZs offer effective approximations to the original graph, their inherent overlap imposes constraints on the search space, potentially conflicting with global optimization objectives. Here we show how such limitations can be converted into advantages in the Close Enough Orienteering Problem (CEOP) by aggregating prizes across overlapped neighborhoods. We further extend the classic CEOP with Non-uniform Neighborhoods (CEOP-N) by introducing non-uniform cost considerations for prize collection. To tackle CEOP (and CEOP-N), we develop a new approach featuring a Randomized Steiner Zone Discretization (RSZD) scheme coupled with a hybrid algorithm based on Particle Swarm Optimization (PSO) and Ant Colony System (ACS) — CRaSZe-AntS. The RSZD scheme identifies sub-regions for PSO exploration, and ACS determines the discrete visiting sequence. We evaluate the RSZD’s discretization performance on CEOP instances derived from established CETSP instances and compare CRaSZe-AntS against the most relevant state-of-the-art heuristic focused on single-neighborhood optimization for CEOP instances. We also compare the performance of the interior search within SZs and the boundary search on individual neighborhoods in the context of CEOP-N. Our experimental results show that CRaSZe-AntS can yield comparable solution quality with significantly reduced computation time compared to the single neighborhood strategy, where we observe an averaged 140.44% increase in prize collection and 55.18% reduction of algorithm execution time. CRaSZe-AntS is thus highly effective in solving emerging CEOP-N, examples of which include truck-and-drone delivery scenarios.

A Sim-Learnheuristic for the Team Orienteering Problem: Applications to Unmanned Aerial Vehicles

In this paper, we introduce a novel sim-learnheuristic method designed to address the team orienteering problem (TOP) with a particular focus on its application in the context of unmanned aerial vehicles (UAVs). Unlike most prior research, which primarily focuses on the deterministic and stochastic versions of the TOP, our approach considers a hybrid scenario, which combines deterministic, stochastic, and dynamic characteristics. The TOP involves visiting a set of customers using a team of vehicles to maximize the total collected reward. However, this hybrid version becomes notably complex due to the presence of uncertain travel times with dynamically changing factors. Some travel times are stochastic, while others are subject to dynamic factors such as weather conditions and traffic congestion. Our novel approach combines a savings-based heuristic algorithm, Monte Carlo simulations, and a multiple regression model. This integration incorporates the stochastic and dynamic nature of travel times, considering various dynamic conditions, and generates high-quality solutions in short computational times for the presented problem.