Projection Technique Research Articles

Derivation of a generalized Langevin equation from a generic time-dependent Hamiltonian

Abstract The traditional Mori-Zwanzig formalism yields equations of motion, so-called generalized Langevin equations (GLEs), for phase-space observables of interest from the microscopic dynamics of a many-body system governed by a time-independent Hamiltonian using projection techniques. By using time-ordered propagators and time-independent projection operators, we derive the GLE for a scalar observable from a generic time-dependent Hamiltonian. The only restriction in our derivation is that the time-dependent part of the Hamiltonian and the observable of interest depend on spatial phase-space variables only. If the observable obeys Gaussian statistics and the time-dependent part of the Hamiltonian can be expressed as an odd power of the observable, the friction memory kernel in the GLE becomes proportional to the second moment of the complementary force, as is the case for a time-independent Hamiltonian in the Mori-Zwanzig formalism.

A brief review of reduced order models using intrusive and non‐intrusive techniques

AbstractReduced Order Models (ROMs) have gained a great attention by the scientific community in the last years thanks to their capabilities of significantly reducing the computational cost of the numerical simulations, which is a crucial objective in applications like real time control and shape optimization. This contribution aims to provide a brief overview about such a topic. We discuss both a classic intrusive framework based on a Galerkin projection technique and hybrid/non‐intrusive approaches, including Physics Informed Neural Networks (PINN), purely Data‐Driven Neural Networks (NN), Radial Basis Functions (RBF), Dynamic Mode Decomposition (DMD) and Gaussian Process Regression (GPR). We also briefly mention geometrical parametrization and dimensionality reduction methods like Active Subspaces (ASs). Then we test the performance of such approaches in terms of efficiency and accuracy against three academic test cases, the lid driven cavity, the flow past a cylinder and the geometrically parametrized Stanford Bunny. Moreover, we also briefly present some preliminary results related to a more complex case involving an industrial application.

La relación con el saber en docentes de la Facultad de Psicología de la Universidad de Buenos Aires. Una aproximación desde la clínica

The general objective of this research, which is part of the Master’s thesis in University Teaching, was to investigate the relationship between the knowledge of teachers of Psychology at the University of Buenos Aires and its possible connection to teaching practices. The specific objectives were: to describe the teaching methods of teachers as they describe them and as they can be interpreted from the analysis of their proposals; to describe and characterize the teachers’ conceptions of knowledge; to describe and characterize their way of emotional connection with knowledge; and to establish possible meaningful connections between teaching modalities, and in particular the forms that knowledge takes within them, and the emotional connections of the teachers with knowledge. Through the analysis of interviews, projective techniques and classroom observations, a series of subjective features of the teachers’ relationship with knowledge were identified, which were related to certain forms related to knowledge transmission in the classroom. We understand this relationship as a recursive one, in which the way of relating to knowledge is transformed with each experience, just as each experience produces updates in the way in which each teacher relates to knowledge. This article will present one of the cases analyzed.

Assessment of rock slopes stability using stereographic projection along Wadi Shorshirin in Zurbatiya region/ east of Iraq

Wadi Shorsirin is located in the Wasit governorate east of Iraq, it is considered a promising tourist site for the people who live close to this area. The research aims to study the rocky slopes and assess their stability, along the two banks of Wadi Shorhirine, by choosing five stations distributed where the failure occurs and to determine the expected rock failure along this valley. In addition, determine the factors that affect stability, such as discontinuities, rock resistance, and weathering to take them into account when establishing engineering projects such as roads, and tourist complexes. The stereoscopic projection technique was used to analyze the stability of these rocky slopes and showed there are two types of slopes which are oblique lateral and orthogonal slopes. Toppling and rockfall were found within the study area, in addition to the rolling. Differential eroded in the foot slope was the main reason for the failure, moreover, the existence of joint sets. The rocks of the slopes were evaluated based on the unconfined compressive strength deduced from the point load test into the moderately strong and strong.

Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data

Context. Hot subdwarf stars are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. In the Hertzsprung-Russell Diagram they are located by the blue end of the Horizontal Branch. Most models agree on a quite probable common envelope binary evolution scenario in the Red Giant phase. However, the current binarity rate for these objects is yet unsolved, but key, question in this field. Aims. This study aims to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence techniques and data from the third Gaia data release (GDR3). The results will be compared with those obtained previously using Virtual Observatory techniques on coincident samples. Methods. The methods used for hot subdwarf binary classification include supervised and unsupervised machine learning techniques. Specifically, we have used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their colour-magnitude properties. Among these, 2815 objects have Gaia DR3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). In order to ensure spectral quality, previously to SOM and CNN classification, our 2815 BP/RP set were pre-analysed with two different approaches: the cosine similarity technique and the Uniform Manifold Approximation and Projection (UMAP) technique. Additional analysis onto a golden sample of 88 well-defined objects, is also presented. Results. The findings demonstrate a high agreement level (∼70–90%) with the classifications from the Virtual Observatory Sed Analyzer (VOSA) tool. This shows that the SVM, SOM, and CNN methods effectively classify sources with an accuracy comparable to human inspection or non-AI techniques. Notably, SVM in a radial basis function achieves 70.97% reproducibility for binary targets using photometry, and CNN reaches 84.94% for binary detection using spectroscopy. We also found that the single–binary differences are especially observable on the infrared flux in our Gaia DR3 BP/BR spectra, at wavelengths larger than ∼700 nm. Conclusions. We find that all the methods used are in fairly good agreement and are particularly effective to discern between single and binary systems. The agreement is also consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods also appear effective for detecting peculiarities in the spectra. While promising, challenges in dealing with uncertain compositions highlight the need for caution, suggesting further research is needed to refine techniques and enhance automated classification reliability, particularly for large-scale surveys.

Quantifying sandstone crack extension and expansion via deep learning methods

This study investigates the damage evolution of sandstone under loading stresses, which can lead to irreversible plastic deformation and reduced project stability service life. To achieve this objective, we identify and quantify the emergence and expansion process of sandstone cracks using deep learning methods. Specifically, our approach employs a crack segmentation model enhanced by an attention mechanism, achieving a remarkable effective crack detection MIoU of 93.38. Through the application of connectivity domain analysis, skeleton extraction, and orthogonal projection techniques, we extract crucial crack parameters, which are subsequently formulated into equations to determine their actual dimensions. These evolving parameter variations serve as the foundation for predicting the sandstone's entire damage progression, encompassing both microscopic crack growth and eventual catastrophic failure preceding macroscopic manifestation of damage. Validation of our method through uniaxial compression tests on sandstone specimens confirm its efficacy and practical applicability. In conclusion, this study presents a novel framework for quantifying sandstone crack evolution, providing invaluable insights into the intricate mechanisms governing damage fracture progression in rocks.

Optimizing Efficiency: A Comparative Analysis of Project Management Tools with a Business Case Study of Trello Implementation

This paper provides functionality, usability, and suitability for various business environments of several well- known project management software are all thoroughly compared in this study. This review aims to highlight each tool's advantages and disadvantages while providing useful advice for companies looking to streamline their project management processes. The paper includes a business case study that focuses on the use of Trello in a digital marketingcompany in addition to comparative analysis. The company used Trello to improve its project management skills after encountering difficulties with task tracking, communication, and project deadlines. The installation of Trello is described in depth in the paper, from initial setup and customization to staff acceptance and training, including the incorporation of key performance indicators (KPIs) to gauge the tool's effectiveness. Task completion rate, project delivery time, customer happiness, teamwork, and campaign success indicators are among the KPIs considered in this study. The results show that task management and teamwork have significantly improved, leading to better project outcomes. Nevertheless, difficulties including early staff reluctance, a learning curve, and Trello's constrained reporting features were also faced and resolved. Ultimately, this paper addresses the challenges and solutions that come up during the implementation process and shows how choosing the appropriate project management tool may change company processes, increase team productivity, and deliver measurable advantages. Keywords: Project Management, stakeholders, Project management tools and techniques (PMTT), Planning Methods, Project scheduling

Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG

This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The comprehensive database utilized Raman spectra from all 594 blood serum samples. Machine learning investigations were carried out using the Scikit-Learn library and were implemented in Python, and the characteristics of Raman spectra of positive and negative SARS-CoV-2 samples were extracted using the Uniform Manifold Approximation and Projection (UMAP) technique. The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). The kNN model led to a sensitivity of 0.943, specificity of 0.9275, and accuracy of 0.9377. This study showed that combining Raman spectroscopy and a machine algorithm can be an effective diagnostic method. Furthermore, we highlighted the advantages and disadvantages of each algorithm, providing valuable information for future research.

An estimation method for vision-based autonomous landing system for fixed wing aircraft

In this paper, we propose a vision-based estimation method for autonomous landing of fixed-wing aircraft for Advanced Air Mobility. The concept of autonomous flight with minimal human intervention has gained significant interest with a particular focus on autonomous landing. The goal is to overcome the limitations of existing instrument landing systems and reduce reliance on GNSS during aircraft landing. The proposed system leverages the following techniques for high-performance and real-time operations in various real-world environments and during all stages of landing. A deep learning segmentation model was directly learned, created, and used for robust runway recognition performance against environmental changes around the runway. Algorithms were designed to adapt to different flight stages considering the varying information offered by image data when the aircraft is in mid-air and on the ground. The relative lateral position from the aircraft to the runway was estimated using bird’s-eye-view conversion and inverse projection techniques instead of conventional perspective-n-point methods for reducing the recognition error occurring from the conversion between 2D image and 3D world. Finally, the mixed-precision technique was used for the segmentation model to improve inference speed and ensure real-time operation in real-world deployment. Estimation stability and reliability were improved by using a Kalman Filter to cope with sensor uncertainties caused by the real-world flight environment. Consequently, we show that the average error in the lateral position estimation by the proposed method is comparable to the accuracy of a single GNSS and demonstrate successful autonomous landing of our test aircraft with a set of flight tests.

Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques

Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques

Hybrid random projection technique for enhanced representation in high-dimensional data

This paper introduces a novel hybrid random projection method (HRP) that effectively combines the strengths of normal random projection (NRP) and plus-minus one random projection (PMRP). By incorporating a blending parameter, HRP optimises the contributions of the NRP and PMRP, reducing the dimensions of the data while ensuring that the structure of the data is preserved. Conventional methods, such as NRP and PMRP, are recognised for their benefits; however, they also have limitations. NRP is generally accurate but can encounter difficulties with certain data types or noise. The PMRP is simple, but occasionally fails to capture complex relationships within the data. The performance of HRP is investigated through comprehensive evaluations, including simulations and real-world data analyses from key machine learning datasets, such as period change, toxicity, MNIST, and human activity recognition (HAR). We examine various factors such as sample size, dimensions of the original and reduced data, and sparsity. Distance distortion is the primary metric utilised to measure performance, and indicates how well the dataset structure is maintained after dimension reduction. In every test, HRP consistently demonstrates the least distance distortion, performing superiorly to the NRP and PMRP. This is true for a range of scenarios and datasets. HRP represents a significant advancement in dimension reduction techniques. Its ability to minimise information loss during the reduction process demonstrates its potential as a powerful tool for managing complex high-dimensional data in practical applications. This renders HRP an important development in the fields of machine learning, intelligent systems, and artificial intelligence, offering improved efficiency and precision in data analyses.

Эмоциональные коммуникации в детско-родительских отношениях и жизнеспособность будущей взрослой семьи

<p style="text-align: justify;"><strong>Objective</strong>. To analyze the contribution of the most immediate contextual influences (child-parent emotional communications) to the resilience of the adult family (using the samples from Belarus and Russia). <strong>Background. </strong>Understanding the role of emotional dysfunctions in parent-child relationships in the context of Russian and Belarusian cultures helps to identify the conditions for the resilience of a future adult family, which can become the basis for social family policy at the state level and improvement of the practice of psychological assistance to families. <br><strong>Study design. </strong>The study was conducted online using the snowball method. Multiple regression analysis was conducted to determine the impact of dysfunction in parental families, intensity of the negative events, age of respondents and number of children on family resilience. Children's experiences of adults are illustrated by the results of projective technique. <br><strong>Participants. </strong>The study involved 853 respondents (595 people from the Russian Federation, 258 from the Republic of Belarus) aged from 18 to 77 years (average age 36 ± 12 years, median = 36 years), 90,5% female. <br><strong>Measurements. </strong>Family resilience was assessed using Gusarova’s et al. “Family Resilience Assessment Scale” (FRAS-RII). Dysfunctions in parental families were determined according to Kholmogorova’s et al. “Family Emotional Communications” and the projective technique of Odintsova et al. “Space of Trees and Light.” The intensity of negative events in families was assessed on a scale from 1 to 10. <br><strong>Results. </strong>Dysfunctions in parental families make a significant contribution to the resilience of the future adult family (up to 20% of explained variance). Family perfectionism has a positive effect, and the elimination of emotions has a negative impact on the resilience of adult families in the context of two cultures. In Russian families of origin there is over-involvement, and in Belarusian families criticism negatively affects the resilience of respondent’s families. <br><strong>Conclusions. </strong>The critical atmosphere in parental families, the high intensity of adverse events in Belarusian families, and overprotection in the parental families of Russians contribute to a decrease in families’ resilience. The presence and number of children in a family contributes to the family resilience in both countries. Culture and traditions are a solid basis for the majority of Russians and Belarusians.</p>

Static or dynamic pear shapes in radioactive nucleus 224Rn?

We report a comprehensive study on low-lying parity doublet states of 224Rn by mixing both quadrupole- and octupole-shaped configurations in multireference covariant density functional theory, in which broken symmetries in configurations are restored using projection techniques. The low-lying energy spectrum is reasonably reproduced when the shape fluctuations in both the quadrupole and octupole shapes are considered. Electric octupole transition strength in 224Rn is found to be B(E3;31-→01+)=43\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B(E3; 3^-_1 \\rightarrow 0^+_1)=43$$\\end{document} W.u., comparable to that in 224Ra, whose data are 42(3) W.u.. Our results indicate that 224Rn shares similar low-energy structure with 224Ra despite the excitation energy of first 3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3^-$$\\end{document} state of the former nucleus is higher than that of the latter. This study suggests 224Rn is a candidate for the search for permanent electric dipole moment.

THE POSSIBILITIES OF PROJECTIVE TECHNIQUES IN DIAGNOSING THE CURRENT PSYCHOLOGICAL STATE OF THE GUARD SERVICE PERSONNEL

The article presents the results of a study on the potential of projective techniques in diagnosing the current psychological state in the process of accompanying the combat service of the internal guard. In the course of the study, an interesting array of data was obtained showing the effectiveness and potential effect of using these techniques in solving service maintenance tasks.

Empirical validation of the use of projective techniques in psychological testing using Galois fields

It is shown that the problem of the adequacy of psychological testing methods, which are varieties of “projective techniques”, is far from being universally recognized. To solve this problem, we used an empirical method based on collecting of statistics of respondents’ answers, as well as a method of analyzing this statistics by means of representing permutations through functions taking values in Galois fields. Based on experimental data, it is shown that the distribution of respondents’ answers to a test in which they are asked to rank pictures in accordance with their own preferences is not homogeneous. Experimental data show that there are answer options that are statistically most common. An interpretation of testing is proposed in which passing the test is considered as “connecting” an external additional layer to the neural network formed by the respondent’s brain. In accordance with this interpretation, the most frequently occurring answer options can be considered as the basis for the formation of classification characteristics. It is shown that during using tests of this type it is advisable to take into account the code distances between the answer of a particular respondent and the codes corresponding to the most frequently occurring sequences. The possibilities of generating psychological tests directly based on experimental data and images generated by neural networks are discussed.

Robust function guided color encoded single fringe pattern and unwrapping method

Fringe Projection Profilometry (Fringe Projection Profilometry，FPP), renowned for its speed, precision, and automation advantages, has found extensive applications in industries such as mechanical processing and quality inspection. A critical challenge within fringe projection techniques involves ensuring robust unwrapping while minimizing the necessary fringe images. While various unwrapping methods exist to reduce the number of projected images, there is a deficiency in efficient unwrapping methods developed under the guidance of error analysis theory. In this study, we derive a robustness function from FPP's error theory to assess the unwrapping robustness of different encoded images. Using this robustness function, we select highly robust single-color encoded fringe images and achieve unwrapping robustness with minimal training cost based on a single fringe pattern. Furthermore, this unwrapping process does not necessitate the establishment of complex training databases, as the K-nearest neighbor (K-Nearest Neighbor，KNN) algorithm learns the correspondence between image tricolor grayscale values and fringe orders from multi-frequency unwrapping method. Finally, experimental validation through a checkerboard measurement confirms that the proposed method can achieve robust unwrapping with low training costs using only one encoded fringe, thereby affirming the effectiveness of the introduced robustness function and the feasibility of the designed color encoded single fringe pattern unwrapping method.

SPECIFICITY OF CHILDREN'S SOCIALIZATION IN COMPLETE AND PARENT FAMILIES

The article describes the specifics of the socialization process of children in complete and incomplete families. The research is based on the use of various methods: parental surveys and psychological tests. The main goal was to determine the level of anxiety in children and their individual characteristics influencing the socialization process.The study showed that children from incomplete families are characterized by a higher level of anxiety than children from complete families. During the study of personality traits of children according to R. Kettell's questionnaire, it was found that most children living in incomplete families and for various reasons raised by one parent show significant differences in some traits and behaviour, which may result from the fact that they have an incomplete picture of the family environment, unlike children from complete families. Such traits as emotional stability, normative behaviour and self-control are not characteristic of this category of children.The projective technique "My Family" made it possible to assess the specifics of relations between children and members of complete and incomplete families. Analysis of the research results showed that children with behavioural disorders more often appear in the drawings of children who have experienced a divorce of their parents than children in harmonious interpersonal interaction with their parents. The questionnaire "Analysis of family relations" (Eidemiller, Yustyckis) allowed to diagnose dysfunctions in the system of mutual influence of family members on each other and to detect errors in the parenting style chosen by the parents. Analysis of the answers of complete and incomplete families surveyed using this method and their comparison with the description of parenting styles allows to state that in incomplete families the features of excessively dominant care, excessive protection and excessive indulgence, emotional alienation prevail. Parents who raise their children separately or alone most often choose an "indulgent" parenting style.

Transliterating Javanese Script Images to Roman Script using Convolutional Neural Network with Transfer Learning

The Javanese script holds immense cultural significance within Indonesia despite its diminishing usage in contemporary contexts. Its presence remains notable in specific regions of Java and remains integral to many historical documents and texts. Consequently, there is an urgent need for a transliteration system adept at converting Javanese script into contemporary scripts like Roman or Indonesian, thereby contributing to preserving Java's linguistic and cultural legacy. However, reading or transliterating Javanese script can be time-consuming, especially for longer texts, presenting considerable challenges for non-native readers. This study aims to develop an effective transliteration system for converting Javanese script into Roman script. This system addresses the pressing need to preserve Java's linguistic and cultural heritage by facilitating the readability and accessibility of Javanese script, especially for non-native readers. This study introduces an Optical Character Recognition (OCR) system tailored to identify Javanese script characters and transcribe them into Roman characters, explicitly focusing on fundamental nglegena and sandhangan swara characters. Individual characters are isolated by leveraging horizontal and vertical projection techniques, facilitating subsequent classification using a Convolutional Neural Network (CNN) employing transfer learning methodologies. The system's achievement of an impressive average similarity score of 90.78% is noteworthy, with the Xception architecture demonstrating superior efficiency in transliteration tasks. Implementing such a system harbors significant promise in safeguarding the Javanese script and enhancing its accessibility to a broader audience. This research contributes substantially to preserving and propagating Indonesia's rich cultural and linguistic heritage amidst the digital age.

Структура психологических последствий контртеррористической деятельности: дифференцированный подход к реабилитации

Introduction. The article deals with the structure of psychological consequences affecting the officers of internal affairs bodies who took part in the counter-terrorist operation (as part of combined units) in the territory of the Chechen Republic. The relevance of the research is determined by the necessity of substantiation of indications for a differentiated approach to medical and psychological rehabilitation of the law enforcement officers having performed service and combat tasks in special conditions. Methods. The research involved 110 officers of internal affairs bodies, average age 29,38±6,5 years; average period of being in the zone of armed conflict 146,9±79,8 days; number of missions 1,41±0,65. Research methods: anonymous questionnaire, standardised multifactor method of personality research, 16-factor personality questionnaire of R. Kettell, accentuation type identification test of H. Smishek, projective technique ‘Hand-test’, drawing association test of S. Rosenzweig, traumatic stress intensity questionnaire of I. O. Kotenev. Statistical processing of the results was carried out by methods of descriptive and factor analysis (method of principal components and varimax-rotation of the factor structure). Results. The author found six factors that explain 81.89 % of the total dispersion of traits in the structure of psychological consequences of counter-terrorist activity. Two of them - ‘posttraumatic’ (21.4 %) and ‘stuck-posttraumatic’ (7.57 %) factors - include symptoms of posttraumatic stress disorder, as well as predictors contributing to its prolonged flow. Signs of ‘subthreshold’ (prenosological) posttraumatic stress disorder, in the form of characteristic personality traits were identified: pessimism, psychasthenia, neurotic control, rigidity, impulsiveness, aggressiveness, cyclothymia, emotional ambivalence, ‘getting stuck’ on affect, frustration fixation on self-protection, anxiety, suspiciousness, sensitivity, guilt, social introversion, passivity, egocentricity. They are part of five out of six factors (‘posttraumatic’, ‘unproductive frustration response’, ‘demonstrative-neurotic’, ‘rigid-affective’, ‘stuck-posttraumatic’) and need both timely and diverse measures of medical and psychological rehabilitation. Psychological peculiarities included in the ‘hyperthymic-conform’ factor (12.06% of the variance) can be compensated by psychological correction. The author analysed foreign and national methods of psychological rehabilitation of posttraumatic stress disorder, identified psychotherapeutic ‘targets’ of influence and proposed measures of psychological rehabilitation of internal affairs officers with regard to the factor structure of the consequences.

Controlling the scatterplot shapes of 2D and 3D multidimensional projections

Multidimensional projections are effective techniques for depicting high-dimensional data. The point patterns created by such techniques, or a technique’s visual signature, depend — apart from the data themselves — on the technique design and its parameter settings. Controlling such visual signatures — something that only few projections allow — can bring additional freedom for generating insightful depictions of the data. We present a novel projection technique — ShaRP — that allows explicit control on such visual signatures in terms of shapes of similar-value point clusters (settable to rectangles, triangles, ellipses, and convex polygons) and the projection space (2D or 3D Euclidean or S2). We show that ShaRP scales computationally well with dimensionality and dataset size, provides its signature-control by a small set of parameters, allows trading off projection quality to signature enforcement, and can be used to generate decision maps to explore the behavior of trained machine-learning classifiers.