Complex Ones Research Articles

Comparative Analysis of Approaches to the Formation of an Institutional Framework for the Development of a Changing Resource Base for Hydrocarbon Production (on the Example of High-viscosity Oils in Alberta (Canada) and the Republic of Tatarstan (Russia))

The modern evolution of the dynamics of the structure of the hydrocarbon resource base in various regions is characterized, as a rule, by the gradual replacement of traditional sources with more complex ones. The latter are currently classified as hard-to-recover reserves (HTRR). The process of mastering and involvement in the development of HTRR is closely related to the transition to an innovative path of development, the creation of new technologies, the strengthening of the role of local knowledge, the accumulation of experience working with non-trivial sources of raw materials, and most importantly, with the efforts cooperation of various participants involved both in scientific and technological processes and in the development of subsurface areas. All of the above is impossible without the formation of an appropriate institutional framework with regional specifics. One example of an approach implemented in this area is the Canadian province - Alberta. There is also some experience in this field in Russia – in the Republic of Tatarstan. In a comparison with the initial conditions and approaches to the development of hard-to-recover hydrocarbon resources in these territories shows that there are both common and distinct features within the framework of the implemented approaches. Consideration of the latter is important from the point of view of choosing the directions for the formation a domestic working model in this area.

Landslide topology uncovers failure movements

The death toll and monetary damages from landslides continue to rise despite advancements in predictive modeling. These models’ performances are limited as landslide databases used in developing them often miss crucial information, e.g., underlying movement types. This study introduces a method of discerning landslide movements, such as slides, flows, and falls, by analyzing landslides’ 3D shapes. By examining landslide topological properties, we discover distinct patterns in their morphology, indicating different movements including complex ones with multiple coupled movements. We achieve 80-94% accuracy by applying topological properties in identifying landslide movements across diverse geographical and climatic regions, including Italy, the US Pacific Northwest, Denmark, Turkey, and Wenchuan in China. Furthermore, we demonstrate a real-world application on undocumented datasets from Wenchuan. Our work introduces a paradigm for studying landslide shapes to understand their underlying movements through the lens of landslide topology, which could aid landslide predictive models and risk evaluations.

A comparative analysis of machine learning classifiers in the classification of resonant asteroids

This study explores how well various machine learning classifiers can identify mean-motion resonances in the main belt using supervised learning. The most popular classifiers are assessed: k-Nearest Neighbours, Decision Tree, Gradient Boosting, AdaBoost, Random Forest, and Naïve Bayes. In contrast to previous studies that often relied on default ML configurations, this research conducts a detailed investigation, fine-tuning, and testing of each classifier across various parameters. The results show that simpler models, especially k-Nearest Neighbours and Decision Tree, perform better than more complex ones, particularly in terms of F1 scores. The paper provides guides on selecting features, parameters, and training set sizes for optimal classifier performance and outlines a method for developing effective machine-learning models for asteroid classification.

Consistency in the assessment of postural balance using static stabilometry and scale techniques in older individuals: An observational study

BACKGROUND: In clinical practice, both clinical scales and instrumental methods are used in parallel to assess postural stability and the risk of falls. Literature data on the comparability of postural balance estimates obtained using scale techniques and stabilometry, particularly in older patients, are conflicting.
 AIM: to analyze the consistency in the assessment of postural balance by static stabilometry and scale techniques in older patients.
 MATERIALS AND METHODS: A pilot observational, nonrandomized, single-center study was conducted. The study participants were 39 women aged 60–75 years who did not have diseases or conditions that significantly affected the postural control system. Postural balance was assessed using clinical scales and tests (timed up and go test, classic and a double task; Berg balance scale; Mini-BESTest, balance evaluation systems test; performance-oriented mobility assessment; and falls efficacy scale) and basic spatial, spatiotemporal, and spectral parameters of static stabilometry. A stabilometric study was conducted at the posturological complex Biokinect. Correlation analysis of the data of the scale techniques and stabilometry was performed.
 RESULTS: The correlation analysis showed both the correlation between some parameters of stabilogram and scale techniques and the complete absence of a significant correlation between others. However, even in the presence of a statistically significant correlation, the relationship between the parameters did not exceed the moderate or average level (maximum value of the correlation coefficient, 0.685, p 0.01). The largest correlation was found between the stabilometric parameters and the timed up and go test (correlated with 11 out of 21 parameters) and the falls efficacy scale (10 parameters), and the smallest was found with the Mini-BESTest (5 parameters). The values of the stabilometric parameter (speed of movement of the center of pressure) demonstrated a statistically significant correlation with most of the clinical instruments (4 out of 6).
 CONCLUSION: This study showed low consistency between the basic parameters of static stabilometry and scale techniques in older people. The results of simpler scale techniques for the assessment of postural balance correlated better with stabilometry data than with more complex and multicomponent ones because the total indicator of the latter is a generalized assessment of postural stability and other elements of complex motor behavior associated with postural balance. Therefore, simple scale techniques and tasks should be preferred when assessing the basic postural stability and risk of falls in older people.

Short term wind speed forecasting using artificial and wavelet neural networks with and without wavelet filtered data based on feature selections technique

Wind speed forecasting plays a crucial role in enhancing the efficiency, reliability, and profitability of renewable energy systems. Accurate wind speed forecasting optimizes energy production and grid integration in renewable energy systems. It assists in maintenance scheduling, decision-making in energy markets, and supports risk management for financial planning. This paper investigates the difficult balance between model complexity and forecasting accuracy in wind speed forecasting using Artificial Neural Networks (ANNs) and Wavelet Neural Networks (WNNs). Employing a time series approach, the study develops models for 1-h-ahead wind speed forecasting, utilizing recent averaged data and exploiting correlations between consecutive wind speeds for improved short-term predictions. Models training involve the Backpropagation algorithm, with careful input variable selection to minimize errors. This study emphasizes that simpler ANN and WNN models can outperform complex ones and explores the effectiveness of wavelet filtering techniques and demonstrates the benefits of using wavelet filtering to enhance forecasting accuracy. The robustness of the developed models is validated through K-fold cross-validation, confirming the efficacy of the proposed models. Evaluation across two datasets demonstrates the superiority of the proposed models over the persistence model, with enhancements of 7–14% in RMSE. Additionally, the efficacy of wavelet transforms in enhancing forecasting accuracy compared to regular neural network predictions is emphasized, underscoring the effectiveness of wavelet filtering offering a strategic approach for optimizing wind power management in electrical grids.

Numerical impact of variable volumes of Monte Carlo simulations of heterogeneous conductivity fields in groundwater flow models

The knowledge of aquifer systems, their geological setting, their structure, and subsequent modeling is highly uncertain and is usually faced through Monte Carlo-based methods in hydrogeology. One of the most important uncertainty sources for groundwater models is represented by input hydraulic conductivities, related to the aquifer’s structure. There are no specific rules when simulating hydraulic conductivity fields within Monte Carlo frameworks to instruct numerical models, and information about employed conductivity fields and their numerical convergence is often not given. This technical work aims to fill this gap by investigating the impact of employing conductivity information upon different volumes of Monte Carlo simulations applied to a real case study. Thus, this work estimates the minimum volumes of Monte Carlo hydraulic conductivity fields to be employed in groundwater flow models for achieving numerically stable (i) boundary conditions, (ii) global model performances, and (iii) local ones such as simulated hydraulic heads. The present results aim to be indicative of similar hydrogeological settings and will serve as a basis for more complex ones and for investigating transport problems.

Support of Cancer Patients at the Stage of Early Rehabilitation: Possibilities of Postural Correction and Art Therapy Techniques

Introduction. Early rehabilitation of cancer patients after surgery plays an important role in their recovery and return to normal functioning. One of the tasks of this stage of treatment is to restore physical activity of the body, in particular, through postural correction. The medical aspects of this process are well described in the literature, however, the psychological aspects of the postural correction process are not sufficiently covered. Even fewer studies are devoted to restoring the psychological state of cancer patients. Purpose. Analysis of the possibilities of postural correction of cancer patients and the possibility of using art therapy techniques to normalize the psychological state during early rehabilitation.Theoretical justification. One of the key elements of postural correction is the restoration of the ability to maintain balance. For this purpose, special devices are used – stabilometric platforms. Another important element is the restoration of voluntary movements. For a more effective recovery, it is worth teaching patients the psychological adjustment to exercise movements. When the patient plans a movement, he forms a mental construct “task to be completed”, which promotes more precise movements. Restoration of their stable psychological state of cancer patients is possible, in particular, through art therapy techniques. One of the techniques, coloring mandalas, involves both hemispheres of the patients’ brains, triggering cognitive processes through the activation of fine motor skills. This provides a natural therapeutic process: the brain works initially on simple tasks and gradually moves on to more complex ones. The details of the mandala drawing are thought out in such a way that in the process of working on it, the patient focuses on his inner state – thoughts and feelings.Discussion. The importance of an integrated approach to the rehabilitation of cancer patients is increasingly emphasized in scientific papers. During the early rehabilitation period, the patient is in the most vulnerable state, both physically and psychologically. The use of psychological techniques can ensure that the patient can restore a more holistic image of himself both physically and internally.

Homo- and heterometallic coordinative compounds of Cu(II) and Bi(III) with aminopolycarboxylates and 2-benzoylpyridine thiosemicarbazones

This article deals with the synthesis and study of three homometallic coordination compounds of Cu(II) and four Cu(II) - Bi(III) heterometallic compounds with mixed polydentate ligands, thiosemicarbazone - aminopolycarboxylate (APC), with the general formulas Cu(L)2Cu(APC)·nH2O, Cu(HL){Bi(APC)}2·nH2O and Cu(L)Bi(APC)·nH2O (n = 0 - 13, APC = edta4- and cdta4-), 4-methyl- and 4-ethyl-thiosemicarbazones of 2-benzoylpyridine are in the monodeprotonated (L) or nondeprotonated (HL) forms. The antibacterial activity of heterometallic compounds Cu(HBzPyTSC-4Me){Bi(edta)} 2 (4) and Cu(HBzPyTSC-4Et){Bi(edta)}2·7H2O (6) was determined against Gram-positive strains Staphylococcus aureus (ATCC 25923) and Bacillus cereus (ATCC 11778) and two Gram-negative strains Escherihia coli (ATCC 25922) and Acinetobacter baumannii (BAA- 747). The compounds displayed high activity against Staphylococcus aureus (ATCC 25923), with the minimum inhibitory concentration MIC = 0.24 μg/mL, a value approximately 19 times higher than the activity of Furacilin (4.67 μg/mL). Both compounds have bactericidal activity against Bacillus cereus strain (ATCC 11778) with MIC = 0.97 μg/mL, which is about 4.8 times higher than the activity of Furacilin. The fungicidal activity of the compounds 4 and 6 against Candida albicans strain, expressed in MIC, was found to be 7.81 and 15.62 μg/mL, correspondingly, values of about 4 and 2 times higher than the one of Nystatin (32.0 μg/mL).

Income, education, and other poverty-related variables: A journey through Bayesian hierarchical models

One-shirt-size policy cannot handle poverty issues well since each area has its unique challenges, while having a custom-made policy for each area separately is unrealistic due to limitation of resources as well as having issues of ignoring dependencies of characteristics between different areas. In this work, we propose to use Bayesian hierarchical models which can potentially explain the data regarding income and other poverty-related variables in the multi-resolution governing structural data of Thailand. We discuss the journey of how we design each model from simple to more complex ones, estimate their performance in terms of variable explanation and complexity, discuss models' drawbacks, as well as propose the solutions to fix issues in the lens of Bayesian hierarchical models in order to get insight from data.We found that Bayesian hierarchical models performed better than both complete pooling (single policy) and no pooling models (custom-made policy). Additionally, by adding the year-of-education variable, the hierarchical model enriches its performance of variable explanation. We found that having a higher education level increases significantly the households' income for all the regions in Thailand. The impact of the region in the households' income is almost vanished when education level or years of education are considered. Therefore, education might have a mediation role between regions and the income. Our work can serve as a guideline for other countries that require the Bayesian hierarchical approach to model their variables and get insight from data.

Information transfers and flows in Markov chains as dynamical causal effects.

A logical sequence of information-theoretic quantifiers of directional (causal) couplings in Markov chains is generated within the framework of dynamical causal effects (DCEs), starting from the simplest DCEs (in terms of localization of their functional elements) and proceeding step-by-step to more complex ones. Thereby, a system of 11 quantifiers is readily obtained, some of them coinciding with previously known causality measures widely used in time series analysis and often called "information transfers" or "flows" (transfer entropy, Ay-Polani information flow, Liang-Kleeman information flow, information response, etc.,) By construction, this step-by-step generation reveals logical relationships between all these quantifiers as specific DCEs. As a further concretization, diverse quantitative relationships between the transfer entropy and the Liang-Kleeman information flow are found both rigorously and numerically for coupled two-state Markov chains.

ARTIFICIAL INTELLIGENCE IN EDUCATION: CASES OF USING CHATGPT 3.5

Formulation of the problem. This research explores the possibilities and peculiarities of employing artificial intelligence in general secondary education. A retrospective analysis of human utilization of artificial intelligence is conducted. Emphasis is placed on the relevance and significance of aligning the education system with contemporary demands and implementing new technological solutions. The focus is on the correctness of queries to systems and the necessity of analyzing work outcomes. Materials and methods. Data collection and processing were conducted during professional development courses for education professionals in the Chernihiv region (Ukraine) through pedagogical observation of course participants and their work with AI. Throughout the study, 34 training sessions were conducted, involving 748 teachers teaching various subjects. Among the showcased cases of AI usage, the most demanded queries among teachers are presented. Results. Examples of text analysis using the generative system ChatGPT 3.5 are provided. The obtained results are analyzed. The findings emphasized the necessity of clearly formulating queries and providing comprehensive input data and complete texts for analysis. In such cases, the artificial intelligence system can provide more accurate responses. This study demonstrated that the system handles this task quite successfully when providing the text of a program code and requesting commentary. However, solving mathematical problems poses specific difficulties, and obtaining the correct answer is only sometimes achievable. That reiterates the critical approach toward the system's obtained work outcomes. Conclusions. ChatGPT 3.5 can correctly analyze well-known facts and provide accurate information about them, but if the facts are local, the system cannot cope with the task. Simple math problems and tasks are performed correctly, but more complex ones already cause difficulties. It is essential not only to write prompts correctly but also to present input data. Such tasks as paraphrasing, translation, and changing the text's tone demonstrate modern AI systems' capabilities. ChatGPT 3.5 can become a good teacher's assistant or assistant for preparing various documentation. Requests for help in writing lesson plans, preparation of educational activities, formation of project topics, and technological maps of projects are carried out by the system at a high level.

Korean Syntactic Complexity Analyzer (KOSCA): An NLP application for the analysis of syntactic complexity in second language production

Given the lack of computational tools available for assessing second language (L2) production in Korean, this study introduces a novel automated tool called the Korean Syntactic Complexity Analyzer (KOSCA) for measuring syntactic complexity in L2 Korean production. As an open-source graphic user interface (GUI) developed in Python, KOSCA provides seven indices of syntactic complexity, including traditional and Korean-specific ones. Its validity was tested by investigating whether the syntactic complexity indices measured by it in L2 written and spoken production could explain the variability of L2 Korean learners’ proficiency. The results of mixed-effects regression analyses showed that all seven indices significantly accounted for learner proficiency in Korean. Subsequent stepwise multiple regression analyses revealed that the syntactic complexity indices explained 56.0% of the total variance in proficiency for the written data and 54.4% for the spoken data. These findings underscore the validity of the syntactic complexity indices measured by KOSCA as reliable indicators of L2 Korean proficiency, which can serve as a valuable resource for researchers and educators in the field of L2 Korean learning and assessment.

Dynamics of dialect change in urban communities: Analysis of linguistic variation and evolution of Polish urban dialects based on data from local libraries

The relevance and necessity of the study of this topic lies in the fact that, in comparison with other lexical groups, dialectal linguistic units are peripheral and can easily be lost. The purpose of this study was to thoroughly examine and analyse the emergence and development of dialects in different regions of Poland. The study employed structural-functional and dialectical methods, the method of synthesis, logical and comparative analysis, the method of generalisation, as well as comparative, statistical, and system analysis. As a result, it was found that linguists distinguish three urban dialects of Polish: Warsaw, Poznan, and Krakow. The Warsaw dialect is the most widespread urban Polish dialect. Over the past few decades, the Warsaw dialect has undergone considerable changes. These changes can be noticed on several levels: the mazurka has disappeared, the accent and intonation have been simplified, and some terms are pronounced differently. Simple past tense forms are used more often than complex ones, and the grammar generally becomes simpler. The number of English and Russian borrowings is growing. Conventional phrases and expressions are becoming less common, while slang and colloquial terms are becoming more common. Unfortunately, Polish urban dialects are under threat of extinction. Local libraries are crucial for the preservation of dialects, as they collect resources on urban dialects and hold events to promote their use. The study is practically important, since all the theoretical provisions, conclusions, and recommendations can be used by philologists to improve their knowledge of the dialectical vocabulary of Poland

Patrizi, panpsychism, and the Presocratics

ABSTRACT The main aim of the article is to show how panpsychism, that is, the idea the everything in the world is endowed with a soul, was varied even during the periods in the history of philosophy when it flourished. In the Renaissance, I focus on Francesco Patrizi: he coined the term, which originally meant that everything is ensouled. The article starts by an investigation of Patrizi’s attempt to trace panpsychism back to the most ancient thinkers. His conclusions are, in general, in agreement with current scholarly assessment of early Greek philosophers, whose views I attempt to reconstruct in a kind of survey. A closer comparison, however, shows significant differences between Patrizi’s and today’s account of the most ancient conception of panpsychism. While Patrizi uses the concept to state that the world as a whole is ensouled, early Greek philosophers understood it to mean that every thing in the world possesses a particular soul. From a broader perspective, it is clear that, while Patrizi builds on the notion of all-embracing ancient philosophy, modern scholarship assumes a more historical account of ancient thought characterised by a gradual progress from simple, more empirically based concepts, to more complex and metaphysical ones.

Multigram-scale chemoenzymatic synthesis of diverse aminopolycarboxylic acids as potential metallo-β-lactamase inhibitors.

Toxin A, a precursor to naturally occurring aspergillomarasmine A, aspergillomarasmine B, lycomarasmine and related aminopolycarboxylic acids, was synthesized as the desired (2S,2'S)-diastereomer on a multigram-scale (>99% conversion, 82% isolated yield, dr > 95 : 5) from commercially available starting materials using the enzyme ethylenediamine-N,N'-disuccinic acid lyase. A single-step protection route of this chiral synthon was developed to aid N-sulfonylation/-alkylation and reductive amination at the terminal primary amine for easy derivatization, followed by global deprotection to give the corresponding toxin A derivatives, including lycomarasmine, in moderate to good yields (23-66%) and with high stereopurity (dr > 95 : 5). Furthermore, a chemoenzymatic route was developed to introduce a click handle on toxin A (yield 72%, dr > 95 : 5) and its cyclized congener for further analogue design. Finally, a chemoenzymatic route towards the synthesis of photocaged aspergillomarasmine B (yield 8%, dr > 95 : 5) was established, prompting further steps into smart prodrug design and precision delivery. These new synthetic methodologies have the prospective of facilitating research into the finding of more selective and potent metallo-β-lactamase (MBL) inhibitors, which are urgently needed to combat MBL-based infections.

MAPPING GAMIFICATION TRENDS IN HRM: A BIBLIOMETRIC EXPLORATION USING R STUDIO

Purpose: Gamification, the application of game-like elements in non-gaming contexts, has quickly become popular as one of the most effective persuasive strategies for encouraging useful behavioral changes in users. The gamification research in the field of HRM is expanding swiftly, moving from basic and fundamental questions like what and why to more complex ones like how, when, and when not to gamify for the employees. It still faces theoretical and empirical challenges to demonstrate the practical effects of gamification and strengthen the guiding principles for meaningful gamification designs. The paper aims to do a bibliometric analysis to characterize the structure and evolution of gamification as a scientific field. Design/Methodology: Using the RStudio package, a bibliometric analysis of articles is conducted in gamification by extracting data from the Scopus database for sixteen years (2007-2023). The conceptual, intellectual, and social network structures of gamification research, as well as the dynamic and evolutionary features of the field, using techniques from science mapping and bibliometric performance analysis, are analyzed. Findings: Bibliometric approaches are used to identify the top authors, top journals, and important topics. A thorough bibliographic network-based content analysis is then conducted. There are three main clusters that show the scope of the specialized field's study. Lastly, the study has identified critical research gaps to guide future research. Practical implications: The purpose of this article is to examine the journal's predominant patterns about articles, authors, institutions, countries, journals, themes, and keywords. The study's findings will assist readers in fully comprehending the topic. Limitations: The database utilized is exclusively from Scopus; it does not include information from other databases like Google Scholar, the Web of Science, etc. The available data is merely for sixteen years, from 2007 to 2023.

Malondialdehyde Assays in Higher Plants.

Malondialdehyde is a three-carbon dialdehyde produced as a byproduct of polyunsaturated fatty acid peroxidation widely used as a marker of the extent of lipid peroxidation in plants. There are several methodological approaches to quantify malondialdehyde contents in higher plants, ranging from the simplest, cheapest, and quickest spectrophotometric approaches to the more complex ones using tandem mass spectrometry. This chapter summarizes the advantages and limitations of approaches followed and provides brief protocols with some tips to facilitate the selection of the best method for each experimental condition and application.

Shape-Based Measures Improve Scene Categorization.

Converging evidence indicates that deep neural network models that are trained on large datasets are biased toward color and texture information. Humans, on the other hand, can easily recognize objects and scenes from images as well as from bounding contours. Mid-level vision is characterized by the recombination and organization of simple primary features into more complex ones by a set of so-called Gestalt grouping rules. While described qualitatively in the human literature, a computational implementation of these perceptual grouping rules is so far missing. In this article, we contribute a novel set of algorithms for the detection of contour-based cues in complex scenes. We use the medial axis transform (MAT) to locally score contours according to these grouping rules. We demonstrate the benefit of these cues for scene categorization in two ways: (i) Both human observers and CNN models categorize scenes most accurately when perceptual grouping information is emphasized. (ii) Weighting the contours with these measures boosts performance of a CNN model significantly compared to the use of unweighted contours. Our work suggests that, even though these measures are computed directly from contours in the image, current CNN models do not appear to extract or utilize these grouping cues.

ПОТОЧНЫЙ МЕТОД ОРГАНИЗАЦИИ СТРОИТЕЛЬНОГО ПРОИЗВОДСТВА

The organization of modern construction production requires the use of effective methods for the use of material and labor resources. One of the progressive methods of planned, continuous and rhythmic construction is the flow method. The peculiarities of this method of organizing production allow the simultaneous construction of several facilities, including large and technologically complex ones. The flow method ensures the rhythmic production of construction products and the equal use of labor resources. The application of the method is one of the advanced technologies of modern construction production, it is widely used both nationwide and abroad, it allows integrating various innovations, using all kinds of communication systems and the latest technologies. The purpose of the study was to analyze the flow method of organizing construction production, make recommendations for increasing the efficiency of the work of the teams, shortening the deadlines for fulfilling orders and reducing the cost of construction projects.

Personalized evaluation of D3-lymph node dissection complexity for right colorectal cancer considering anatomy of superior mesenteric vessels

To provide a personalized approach to D3 lymph node dissection (LND) in right colon cancer using a proper analysis and interpretation of CT angiography of superior mesenteric vessels. An observational cross-sectional study included 34 patients who underwent surgery for right colon cancer with D3 LND along superior mesenteric artery (SMA) and vein (SMV). Characteristics of surgically relevant vascular anatomy were evaluated as 0 or 1 depending on complexity of anatomical characteristic. Patients were classified into groups of standard (0 points) and complex (1-3 points) LND according to complexity of vascular anatomy. SMA and SMV crossed each other at the level of ileocolic artery (ICA) orifice or above in 23.5% of cases (n=8). In 76.5% of cases, they passed parallel to each other (n=25). Ileocolic artery passed posteriorly to SMV in 47.1% (n=16) of cases. A closed type of SMA with a restricted access to D3 area was observed in 41.2% of cases (n=14). Duration of LND significantly differed (43.6±17.8 min in standard cases and 61.9±18.6 min in complex ones, p=0.006). Ascular anatomy can predict complexity of D3 LND in right colon cancer surgery. The complexity scale is an important tool for preoperative planning of D3 LND and identifying technical difficulties associated with vascular anatomy.