Orientation Problem Research Articles

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.

FR-YOLOv7: feature enhanced YOLOv7 for rotated small object detection in aerial images

AbstractFor the problems of weak object feature expression and variable object orientation in aerial image object detection, this paper proposed a feature enhanced YOLOv7 for rotated small object detection in aerial images. Firstly, for the problem of feature loss in the feature extraction stage, the feature enhanced spatial pyramid pooling and cross stage partial connections module was proposed, which effectively boost the feature expression of small object. Secondly, an attention guided max-pooling module was constructed to address the problem of feature loss. Then, the rotated object detection head was introduced to solve the problem of false negatives caused by variable object angles and dense object distribution. Finally, a multi-scale loss function was proposed for improving the detection effects of rotated small objects detection in aerial images. Extensive experiments were conduct on the public datasets of DOTA and University of the Chinese Academy of Sciences-AOD, with the help of the proposed method, we can achieve the detection accuracy that the mean average precision are 79.7% and 98.9%, respectively. Experimental results demonstrate that the proposed method has a significant improvement on the detection of small targets in aerial images.

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

Identity and value orientations of student youth in various ethnocultural groups

The article presents a brief theoretical analysis of the concepts of “identity” and “value orientations”, and provides the results of an empirical study on the problem of identity and value orientations of student youth. The purpose of the article is to present the results of a study of the relationship between the components of identity and value orientations of student youth in the context of cross-cultural analysis. The components of identity and value orientations in samples of Russian and Buryat respondents were studied. Correlations between identity and value orientations have been identified. Participants. 100 Russian and 100 Buryat university students. 200 persons in total. Methods (tools): S. Schwartz’s value questionnaire, identity questionnaire in 16 categories (M.S. Yanitsky et al., 2018), mathematical and statistical processing using Spearman’s rank correlation coefficient rs. The results of the study showed that there are similarities and differences in the correlations of the structure of value orientations of the subjects of the represented ethnocultural groups at two levels (normative ideals and individual priorities). The results obtained showed that the identity and value orientations of students in each group have their own characteristics. But no unique national characteristics were found among representatives of Russian and Buryat youth. Correlation analysis revealed that the hierarchy of values of students from both cultures contains values such as conformity, kindness, stimulation, independence, hedonism, and achievement. Negative correlations were identified between the identity components “Citizen of one’s country”, “Follower of one’s religion”, “Resident of a given city” with the value orientations “Kindness”, “Stimulation”, “Traditions”, “Independence”, “Safety”, “Power”, "Achievements".

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.

STUDENTS' READINESS TO MAKE STRATEGIC DECISIONS: DEVELOPMENT OF PSYCHOLOGICAL AND PEDAGOGICAL DIAGNOSTIC METHODS

The purpose of the study is to theoretically substantiate the structure of students' readiness to make strategic decisions. It is also in formulating a list of statements to assess the level of students' readiness to make strategic decisions based on ideas about this construct. Research objectives: − to actualize the problem of youth strategic orientation in the value-semantic modernization of modern higher education; – to form a cognitive image of assessing the level of students’ readiness to make strategic decisions; – to generalize the idea of the importance of personality-oriented, individual factors in designing and diagnosing of students’ readiness to make strategic decisions. Methodology. Systematic, professionographic, personality-oriented and value-based approaches are used in the article to build psychological and pedagogical methodology; the main research methods are analysis, synthesis, induction and deduction. Results. The authors propose the basis for building a psychological and pedagogical diagnosis of students' readiness to make strategic decisions as a result of the conducted research. The readiness to make strategic decisions gives opportunities for improving educational work with students, which enables to prioritize students' own future activities. It also helps to elaborate the ability to reflect and determine directions for personal and professional development , the skills to rationally allocate resources to achieve significant results. The positions substantiated in the study (the choice of priority life strategies, the ability to reflect on the difficulties arising in life, focus on sociability, on rationally determined individual intentions, values and results, etc.) are advisable to use in educational work with subjects of the cultural and educational space. Information about sponsorship. The research was carried out within the framework of the scientific theme "Research of students' readiness to make strategic decisions in the changing cultural and educational space" (registration number:123112100032-8).

Understanding Drivers’ Behavioral attitudes and Intentions to Use Guidance Systems in Urban Complex Parking Lots Based on the C-TAM-TPB Framework

Multi-function services characteristic of complexes, complicated spatial structure, and inaccurate parking guidance in complex parking lots create many issues for drivers, including orientation problems, excessive parking time, and difficultly finding a parking space close to the desired destination. Complex parking lot guidance systems (CPLGSs), if accurately designed, can potentially make a substantial contribution to alleviate these problems and reduce drivers’ travel time while improving the parking experience. However, the extent of this contribution depends on drivers’ acceptance of this technology. This paper reports on the intention of complex visitors to use CPLGSs. Specifically, an integrated model combining the technology acceptance model and the theory of planned behavior was developed and tested to analyze the factors that can affect drivers’ behavioral attitudes and intentions for CPLGSs. Perceived risk is included as an antecedent on behavioral attitude. Data were collected through an online questionnaire, and 810 valid questionnaires were obtained. Results from a structural equation model show that perceived usefulness, subjective norm, behavioral attitude, and perceived behavioral control are positively connected with behavioral intention, while perceived risk is negatively related to behavioral attitude. Findings on mediators are also discussed. In addition, multi-group differences are analyzed, revealing that the use intention of the age ≤ 40 group is more positively influenced by subjective norms compared with the age &gt; 40 group. Moreover, perceived ease of use has a strong influence on behavioral attitude, especially among individuals who travel to a complex frequently. The theoretical implications in this paper are intended to guide the service improvement of CPLGSs and other parking guidance systems.

A Weighted and Epsilon-Constraint Biased-Randomized Algorithm for the Biobjective TOP with Prioritized Nodes

This paper addresses a multiobjective version of the Team Orienteering Problem (TOP). The TOP focuses on selecting a subset of customers for maximum rewards while considering time and fleet size constraints. This study extends the TOP by considering two objectives: maximizing total rewards from customer visits and maximizing visits to prioritized nodes. The MultiObjective TOP (MO-TOP) is formulated mathematically to concurrently tackle these objectives. A multistart biased-randomized algorithm is proposed to solve MO-TOP, integrating exploration and exploitation techniques. The algorithm employs a constructive heuristic defining biefficiency to select edges for routing plans. Through iterative exploration from various starting points, the algorithm converges to high-quality solutions. The Pareto frontier for the MO-TOP is generated using the weighted method, epsilon-constraint method, and Epsilon-Modified Method. Computational experiments validate the proposed approach’s effectiveness, illustrating its ability to generate diverse and high-quality solutions on the Pareto frontier. The algorithms demonstrate the ability to optimize rewards and prioritize node visits, offering valuable insights for real-world decision making in team orienteering applications.

Cryo-EM Map Anisotropy Can Be Attenuated by Map Post-Processing and a New Method for Its Estimation.

One of the most important challenges in cryogenic electron microscopy (cryo-EM) is the substantial number of samples that exhibit preferred orientations, which leads to an uneven coverage of the projection sphere. As a result, the overall quality of the reconstructed maps can be severely affected, as manifested by the presence of anisotropy in the map resolution. Several methods have been proposed to measure the directional resolution of maps in tandem with experimental protocols to address the problem of preferential orientations in cryo-EM. Following these works, in this manuscript we identified one potential limitation that may affect most of the existing methods and we proposed an alternative approach to evaluate the presence of preferential orientations in cryo-EM reconstructions. In addition, we also showed that some of the most recently proposed cryo-EM map post-processing algorithms can attenuate map anisotropy, thus offering alternative visualization opportunities for cases affected by moderate levels of preferential orientations.

Prediction of Radio Signal Failures of Communication Based Train Operating Systems by Machine Learning Methods

The use of rail systems in urban public transportation has become a necessity for reasons such as time saving, travel comfort and operating costs, especially in cities with high population and road traffic. Communication based train operating systems (CBTC) are used for the safe use of urban rail systems and the maximum capacity of the railway line. In this study, in line with the data collected from the trains on a railway line operated with CBTC, the status of the radio signals that enable the wireless communication of the trains with the trackside signaling equipment was evaluated by machine learning methods, and the situations that may have negative effects on the train operations of the problems at the signal level were evaluate. The problems on the antennas which receives signals from trackside above trains, the poor connection related with fiber optical and LAN cables, the trackside transmitter antenna orientation problems causes decrease on signal levels. It is aimed that to take actions about the problematic signal levels without any negative impact on the passenger comfort and the operation yet. The radio signal losses cause unexpected trains stops and delays. A decision support model has been developed that will offer early solution suggestions to system maintainers in order to intervene first. In conclusion, since it is the first study related with failure prediction by using radio signal levels data on railway signaling system, this study presents an important innovation in terms of literature.

Enhanced EMC-Advantages of partially known orientations in x-ray single particle imaging.

Single particle imaging of proteins in the gas phase with x-ray free-electron lasers holds great potential to study fast protein dynamics, but is currently limited by weak and noisy data. A further challenge is to discover the proteins' orientation as each protein is randomly oriented when exposed to x-rays. Algorithms such as the expand, maximize, and compress (EMC) exist that can solve the orientation problem and reconstruct the three-dimensional diffraction intensity space, given sufficient measurements. If information about orientation were known, for example, by using an electric field to orient the particles, the reconstruction would benefit and potentially reach better results. We used simulated diffraction experiments to test how the reconstructions from EMC improve with particles' orientation to a preferred axis. Our reconstructions converged to correct maps of the three-dimensional diffraction space with fewer measurements if biased orientation information was considered. Even for a moderate bias, there was still significant improvement. Biased orientations also substantially improved the results in the case of missing central information, in particular in the case of small datasets. The effects were even more significant when adding a background with 50% the strength of the averaged diffraction signal photons to the diffraction patterns, sometimes reducing the data requirement for convergence by a factor of 10. This demonstrates the usefulness of having biased orientation information in single particle imaging experiments, even for a weaker bias than what was previously known. This could be a key component in overcoming the problems with background noise that currently plague these experiments.