Projection Technique Research Articles

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

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.

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

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. 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. 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. The findings demonstrate a high agreement level (sim 70-90<!PCT!>) 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<!PCT!> reproducibility for binary targets using photometry, and CNN reaches 84.94<!PCT!> 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 $ sim $700 nm. 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.

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.

Understanding Adaptive Competencies of a Woman in Her 80s: Toward the Development of Therapeutic Guidelines

This article describes the adaptive competencies of an older woman who lived well into her 80s in deprived environments, to serve as a basis for developing therapeutic guidelines for older individuals in contexts of adversities. This retrospective, instrumental case study draws on the theoretical lens offered by the person-environment-fit (PEF) framework in the context of adaptive aging. Multiple data sources (Mmogo-method 1 —a visual projective technique—interviews and extant texts) yielded textual and visual data which were analyzed to present a case record and relevant themes. Personal competencies that enabled this woman’s adaptation were industriousness, courageous and generous caring, and agency. Four types of relationships and associated competencies emerged: ancestral heritage (being rooted), peer group (mobilizing resources), intergenerational (being concerned for continuity), and spiritual (having a commitment script). Therapeutic guidelines for older women include: using a life story approach to identify the nature (flexible/rigid) of the environments and older women’s adaptive competencies; assessing the different environments (e.g. physical, relational) to determine the focus of intervention; challenging unfair social practices; and supporting older women’s agency and active participation to adapt to ‘unfit’ environments while also being aware of what additional complementary support/protection older women would need to embrace adaptive aging.

RBP-DIP: Residual back projection with deep image prior for ill-posed CT reconstruction

The success of deep image prior (DIP) in a number of image processing tasks has motivated their application in image reconstruction problems in computed tomography (CT). In this paper, we introduce a residual back projection technique (RBP) that improves the performance of deep image prior framework in iterative CT reconstruction, especially when the reconstruction problem is highly ill-posed. The RBP-DIP framework uses an untrained U-net in conjunction with a novel residual back projection connection to minimize the objective function while improving reconstruction accuracy. In each iteration, the weights of the untrained U-net are optimized, and the output of the U-net in the current iteration is used to update the input of the U-net in the next iteration through the proposed RBP connection. The introduction of the RBP connection strengthens the regularization effects of the DIP framework in the context of iterative CT reconstruction leading to improvements in accuracy. Our experiments demonstrate that the RBP-DIP framework offers improvements over other state-of-the-art conventional IR methods, as well as pre-trained and untrained models with similar network structures under multiple conditions. These improvements are particularly significant in the few-view and limited-angle CT reconstructions, where the corresponding inverse problems are highly ill-posed and the training data is limited. Furthermore, RBP-DIP has the potential for further improvement. Most existing IR algorithms, pre-trained models, and enhancements applicable to the original DIP algorithm can also be integrated into the RBP-DIP framework.

Re-imagining the Factors Influencing the Choice of Project Management Tools and Techniques in Tanzanian Infrastructure Projects: Elicitation of Practitioners’ Perspectives

Construction project Management tools and Techniques (CPMTT) are vital for the success of construction projects. Nonetheless, there is a paucity of studies that examined the factors influencing the choice of CPMTT in developing countries, particularly, Tanzania. The objective of the study was to analyze the factors influencing the choice of CPMTT in Tanzanian public infrastructure projects, with the aim to fill the knowledge gap. The study was descriptive in nature and employed a mixed method approach. Using purposive and snowball sampling techniques, data was collected from 60 out of 70 Project management practitioners including Consultants and project Coordinators within Dar-es-salaam Metropolitan Development Projects (DMDP) and similar infrastructure projects. Fifty valid responses were received equivalent to 83% response rate and interviews were done. Quantitative data from respondents was analyzed using IBM SPSS v25. The directed content analysis method was employed for qualitative data from 10 Resident Engineers to supplement the quantitative results. Using factor analysis, four groups of organization support, ease of access and usefulness of tools, fulfilment of project objectives, and project environment and idea generation were determined to influence the choice of CPMTT. Project managers frequently choose tools that are easily accessible without considering the organization's project objectives or resource commitment. The findings could be used as a guide for project practitioners to critically assess the areas of weakness and select CPMTT that can help address those weaknesses and hence improve project delivery in Tanzania, an unexplored context. Project managers are called to re-imagine project needs based on priority and select the CPMTT that satisfies the needs based on experience, skills and project objectives as crucial factors in the selection of CPMTT rather than using their discretion. This would improve the delivery of infrastructure projects and, hence, performance outcomes in the construction sector in Tanzania and other emerging countries. This study was limited to DMDP public infrastructure projects in Tanzania. Keywords: Construction Project Management Tools and Techniques (CPMTT), Decision analysis, mixed-methods, Tanzania

Hybrid Hu-Storey type methods for large-scale nonlinear monotone systems and signal recovery

We propose two hybrid methods for solving large-scale monotone systems, which are based on derivative-free conjugate gradient approach and hyperplane projection technique. The conjugate gradient approach is efficient for large-scale systems due to low memory, while projection strategy is suitable for monotone equations because it enables simply globalization. The derivative-free function-value-based line search is combined with Hu-Storey type search directions and projection procedure, in order to construct globally convergent methods. Furthermore, the proposed methods are applied into solving a number of large-scale monotone nonlinear systems and reconstruction of sparse signals. Numerical experiments indicate the robustness of the proposed methods.

The Comparison Between Random Forest and LSTM Models Based on the Gold Price Prediction

The gold price forecast is crucial for financial institutions, investors, mining firms, and associated enterprises. Precise price prediction is especially important in the gold market because of its volatility and unpredictability; this has important consequences for strategy formulation and decision making. In order to give practitioners a better prediction technique to increase the accuracy of market trading, the aim of this study is to assess the predictive effectiveness of the two models in predicting the gold market. The study forecasts gold prices using the Random Forest and Long Short-Term Memory (LSTM) models, comparing their performances using five assessment metrics. The end findings prove that the Random Forest method outperforms the LSTM model in comparison. In particular, the Random Forest model predicts more accurately and with less inaccuracy. The study finds that, while the Random Forest model is more accurate, both the LSTM model and the Random Forest model are appropriate techniques for gold market projections. To increase the precision and consistency of the predictions, future studies will keep refining the LSTM model and look for causes for its subpar performance.

Investigating the internal erosion behavior and microscopic mechanisms of chemically stabilized soil: an experimental study

IntroductionInternal erosion triggered by water pipeline leaks seriously threatens the stability of the urban ground. Hangzhou, a city in Zhejiang Province, China, is facing critical challenges due to urban ground collapse (UGC) caused by internal erosion. However, there is a lack of research on the prevention of UGC by improving the internal erodibility of underground soil. Addressing this issue is of utmost importance to ensure the city’s stability and safety. This paper proposes to improve the internal erodibility of typical sandy silt soils with chemical stabilisers.MethodsThe effects of three chemical stabilisers, lignosulphonate (LS), lime (LI), and lignin fibre (LF), on the critical shear stress (τc) and erosion coefficient (kd) of sandy silt soils were investigated, which from Hangzhou, Zhejiang, China, by the hole erosion test (HET) at different mixing amounts and at different conservation times.ResultsThe findings indicate that LF mainly improves the erosion resistance of sandy silt by increasing τc, and the maximum increase is 2.38 times; LI mainly improves the erosion resistance by decreasing kd, and the maximum decrease is 2.18 times. After adding LS, τc and kd did not change significantly. The scanning electron microscope (SEM) test revealed that the inclusion of LF led to the formation of larger agglomerates in the sandy silt soil. The microstructure of sandy silt soil remained dispersed even after adding LS. Various chemical stabilisers used to improve sandy silt soils exhibited distinct erosion mechanisms. Sandy silt soils improved with LF exfoliated into agglomerates, displaying high resistance to erosion. On the other hand, the sandy silt treated with LF still lacks a protective layer and shows minimal improvements in its ability to withstand erosion. In contrast, the LS-amended sandy silt remains stripped with individual soil particles with insignificant changes in erosion resistance.DiscussionThis study can provide a conceptual framework for choosing foundation treatment techniques in future urban development projects.

Heuristics for the Intelligent Prediction of Population Growth

Population growth is a phenomenon that is inevitable in a life course and the components of population growth are fertility, mortality and migration. Because of the impact population growth exact upon the socio- economic nature of a country, it will be wise to know the future size and distribution of it so that adequate measures can be made to forestall possible problems. There are different population models and algorithms implemented to project population growth, ranging from statistical to machine learning models; these model techniques were very suitable in their time, such as the Malthusian theory, which, first and foremost, gives deeper consideration to the exponential growth model, while many implemented classification and linear regression algorithm. Linear regression machine learning algorithms were considered the most effective algorithm for population growth projection. This study tends to develop a machine learning model that is data-driven mathematically, capable of implementing the data-independent prediction model equation that was used to predict the impact of the Non-pharmaceuticals approach and pharmaceuticals approach (NPA and PA) in mitigating the spread of the Covid-19 virus, and both models were re-modified to project Nigeria population growth. The data-independent prediction model (DIPM) utilized onset data from the NPA and PA interventions to predict the probability of mitigating the spread of the virus for a specific period. The DIPM has the properties of arithmetic and exponential methods. The fusion of these two properties with the aid of machine learning model has further reveal the data-independent prediction model as a conceptualization technique to reflect how data are processed in an algorithm setting in a concrete world, with the support of the Java array list algorithm, what all the statistical models and supervised machines learning model used in the past studies could not achieve, have been accomplished succinctly. The data independent prediction model is a robust technique for both projection and forecasting future population growth as well as proffer answers to historical issues in mathematical modeling of population expansion.