Traditional Research Articles

Sociology of childhood evolution: in the world and in Ukraine

The sociology of childhood, as a separate branch of sociology, emerged about forty years ago. Until then, children and childhood were considered only within the framework of the sociology of family or the sociology of age. In the article, I consider the following questions: what exactly led to the emergence of a new field? How and under the influence of what changed the traditional attitude of social science to children and childhood during the twentieth century. What is the New sociology of childhood and Childhood Studies and how do the views of their proponents differ from traditional ones? What principles unite researchers of the new sociology of childhood and how is the sociology of childhood developing in Ukraine?The childhood phenomenon, despite all the achievements and development of social sciences, is still difficult to define. During the twentieth century, the perception of childhood and the attitude of researchers toward children changed. Sociologists of childhood no longer perceive children as passive, innocent, firmly embedded in the family, and “non-yet-adults” whose main goal is to properly socialize. Now, according to sociologists, children are perceived as active and unique social actors that need to be studied intersectionally, in the context of gender, class, ethnicity, and outside the family. The perception of the child has changed – from a usual future adult in the 1960s to a real member of society that can influence the world of adults in the 1980s. All this happened due to the influence of interactionists schools in sociology, researchers of subcultures and age groups, as well as social movements for women’s rights and civil rights. The new sociology of childhood and childhood studies has set new goals – to explore children’s practices as the activities of socially active agents, using methods that take into account age characteristics, but do not reduce children’s ability to reflect and formulate their own point of view. During the last 40 years, the followers of the new paradigm actively created a new scientific community and tried to change the attitude towards children and childhood not only within the social sciences but also outside of it. They partially succeeded, but many of the announced goals remained unfulfilled. Ukraine and Ukrainian researchers of childhood are taking the first confident steps in this field. We need more translations of world classics into Ukrainian language, as well as the results of original Ukrainian researchers into English. As well as we need more students enrolled in relevant programs and funding for the National Center for Childhood Studies.

Asymptotic results on tail moment for light-tailed risks

In this paper, we focus on the asymptotic behavior of a recently popular risk measure called the tail moment (TM), which has been extensively applied in the field of risk theory. We conduct the study under the framework in which the individual risks of a financial or insurance system follow convolution equivalent or Gamma-like distributions. Precise asymptotic results are obtained for the TM when the individual risks are mutually independent or have a dependence structure of the Farlie-Gumbel-Morgenstern type. Moreover, based on some specific scenarios, we give an asymptotic analysis on the relative errors between our asymptotic results and the corresponding exact values. Since the model settings in this paper are not covered by traditional ones, our work fills in some gaps of the asymptotic study of the TM for light-tailed risks.

Magnetocaloric Properties in Rare-Earth-Based Metal-Organic Frameworks: Influence of Magnetic Density and Hydrostatic Pressure.

Magnetic refrigeration based on the magnetocaloric effect (MCE) in metal-organic frameworks (MOF) is regarded as an attractive approach to create more sustainable cooling systems with higher efficiency than traditional ones. Here, we report a study of the MCE in a series of rare-earth-based MOFs. We have considered the selection of the rare-earth cation by investigating materials belonging to the α-rare-earth polymeric framework-4 (α-RPF-4) MOF family, synthesized with different rare-earth cations, and observed that paramagnetic moment and saturation magnetization play an important role in enhancing the magnetic entropy change ΔSM. The effect of structural parameters has also been considered by investigating three classes of metal-organic Gd materials built up from different types of inorganic secondary building units, including clusters (as in Gd-UiO-66), one-dimensional (as in α-RPF-4), and layered (as in Gd-LRH) conformations. Moreover, the analysis of the hydrostatic pressure influence reveals a significant increase in the -ΔSM and relative cooling power (RCP) with values between 4.3 and 16.3 and 121-509 J/kg. Specifically, the RCPmax found was ∼683 J/kg for Gd-UiO-66, which is higher than the one recently observed for Gd2SiO5 (649.5 J/kg). The present study demonstrates that the engineering of metal-organic framework systems based on high Gd densities may favor enhancing of magnetocaloric responses even at low pressures, thus promoting a new design strategy for efficient cooling devices.

High value-added syngas production by supercritical water gasification of biomass: Optimal reactor design

Supercritical water gasification (SCWG) emerges as a highly encouraging technique for the efficient generation of syngas from biomass feedstock. The performance of the SCWG reactor plays a pivotal role in determining the overall success of the process. This study employs a mathematical modeling and optimization method to design an optimal tubular-type reactor for the SCWG of biomass. First, the reactor is divided into cells, and mathematical models are established to describe the reactions within each cell, as well as the mass balances between cells. Afterwards, the optimal reactor design problem is formulated as a non-linear optimization problem, which involves integration of all the individual models along the reactor length. The objective is to maximize the high heating value (HHV) of gaseous products and simultaneously satisfying multiple constraints. Once the problem is solved, the optimal decision variables (i.e., reactor axial fluid temperature profiles, diameter and length) are obtained. Here, two reactor design scenarios are investigated to showcase the adaptability of this optimization method. The obtained outcomes indicate that non-isothermal reactors with a zigzag-shaped temperature profiles are more advantageous than traditional isothermal ones for high value-added syngas production. Specifically, optimal non-isothermal reactors can achieve a remarkable increase of 50.08% (i.e., 26.94 vs 17.95 MJ/kg glycerol) in the HHV of gaseous products compared to isothermal reactors. These findings provide valuable insights on designing efficient SCWG reactors for industrial high value-added syngas production.

The Establishment of Latvian National Organizations in Krasnoyarsk, 1915-1916, as a Result of the Refugee Movement

The article delves into the impact of the refugee movement during the First World War produced on the self-identity of Latvians living in the Yenisei Province. This research aims to explore the formation and operation of social institutions that encapsulated Latvian self-awareness. These institutions include the Bureau of the Authorized Representative of the Latvian Central Committee for Refugee Assistance in the Yenisei Province, and the Krasnoyarsk Latvian Charitable Society. During this era, the growing national consciousness among the Yenisei Province’s Latvians was evident through an uptick in cultural events, frequently conducted in the Latvian language and organized by Latvians for their community. The author posits that the surge in Latvian self-awareness stemmed from the refugee movements of the First World War. The imperative to support Latvian refugees – offering housing, job opportunities, clothing, financial aid, and more – precipitated the designation of the Authorized Representative and the subsequent establishment of the Bureau under its directory in 1915. This concerted effort to assist the refugees catalyzed the unification of the Latvian intelligentsia, culminating in the foundation of the Latvian national organization, the charitable society, in 1916. The author underscores that the rise of Latvian civic organizations aligns with the broader Siberian trend of creating secular ethnocultural groups alongside the traditional confessional ones. This article caters to both specialists in the ethno-confessional history of pre-revolutionary Siberia and a more general readership.

An artificial neural network emulator of the rangeland hydrology and erosion model

Machine learning (ML) is becoming an ever more important tool in hydrologic modeling. Previous studies have shown the higher prediction accuracy of those ML models over traditional process-based ones. However, there is another advantage of ML which is its lower computational demand. This is important for the applications such as hydraulic soil erosion estimation over a large area and at a finer spatial scale. Using traditional models like Rangeland Hydrology and Erosion Model (RHEM) requires too much computation time and resources. In this study, we designed an Artificial Neural Network that is able to recreate the RHEM outputs (annual average runoff, soil loss, and sediment yield and not the daily storm event-based values) with high accuracy (Nash-Sutcliffe Efficiency ≈ 1.0) and a very low computational time (13 billion times faster on average using a GPU). We ran the RHEM for more than a million synthetic scenarios and train the Emulator with them. We also, fine-tuned the trained Emulator with the RHEM runs of the real-world scenarios (more than 32,000) so the Emulator remains comprehensive while it works specifically accurately for the real-world cases. We also showed that the sensitivity of the Emulator to the input variables is similar to the RHEM and it can effectively capture the changes in the RHEM outputs when an input variable varies. Finally, the dynamic prediction behavior of the Emulator is statistically similar to the RHEM.

Mind mapping in foreign language classes: review of online mind map constructors

The article deals with the peculiarities of the educational technology of mind mapping, reviews several digital applications for creating mind maps, and practically proves the effectiveness of using mind mapping in foreign language classes.The digitization of the educational process, the globalization of knowledge and the availability of educational information arrays, in combination with quarantine restrictions and the war in Ukraine, have presented significant obstacles to foreign language teachers. Although mind mapping technology is relatively new, it has many supporters among linguists. The essence of educational technology based on compliance with the radial type of brain perception of an array of information, which is more natural than linear, is analysed in detail in the article. The appropriate systematicity, structure, simplicity and adaptability of this method of material demonstration, in contrast to the traditional ones, were delivered through the characterization of the main elements of the mental map structure and the basic rules for its creation. The paper provides an overview of three primary online mind map constructors (Gitmind, Mindomo, and Coggle) according to several requirements, adequate for teaching foreign language grammar to first-year students majoring in “Philology”. Based on the review of the basic and target characteristics of mind map applications, the article’s authors recommended the Gitmind constructor and justified the choice. An example of the practical application of mental maps and the application in foreign language classes was also presented in the article. The stages of creating a teaching mind map for presenting a grammar topic were described. The article presents the results of the final survey of students involved in learning with the help of mind maps, which proved the expediency and effectiveness of using mind maps in teaching foreign languages in general, and the optimality of using a digital mind map constructor in particular.

Exploring Heterogeneity in Car-Following Behaviors Based on Driver Visual Characteristics: Modeling and Calibration

To investigate the heterogeneity of car-following behaviors across different vehicle combinations from the perspective of driver visual characteristics, the NGSIM dataset from I-80 and US-101 highways was selected and distinct car-following segments were extracted for analysis. Firstly, all the effective vehicle trajectories were extracted and categorized into different vehicle types based on their widths, resulting in four combination types of car-following segments. Visual angle and its change rate were introduced as variables representing driver visual characteristics. Additionally, one-way analysis of variance (ANOVA) was used to compare these variables with traditional ones. The driver’s visual characteristic variables were then incorporated to improve the full velocity difference (FVD) model. Genetic algorithms were employed to calibrate the model under different car-following types, revealing pronounced behavioral variations. After implementing the enhanced drivers’ visual angle (DVA) model, substantial reductions in calibration and validation errors were observed, with calibration errors decreasing by 51.93% and 42.22% and validation errors decreasing by 56.61% and 45.26%. This indicates the DVA model’s remarkable adaptability and stability. Lastly, through a sensitivity analysis of errors, the DVA model demonstrated greater robustness toward the improved error evaluation function. By integrating drivers’ visual characteristics, this study provides in-depth insights into heterogeneous car-following behaviors, enhancing our understanding of driver behaviors and micro-traffic simulation systems.

The Online Assessment Tools of the MenPas 1.0 Platform, a Reliable and Sustainable Alternative for Psychosocial Research: A Literature Review

New technologies have improved the efficiency and sustainability of psychosocial research. However, online assessment tools may differ from traditional paper-based ones in different domains. This study aims to provide an updated perspective of the online assessment advantages of a psychosocial platform called MenPas 1.0. A literature review was conducted, choosing validated questionnaire studies located in the MenPas 1.0 platform, specifically, motivation and hypnosis questionnaires. A total of five studies showing the psychometric properties of psychosocial evaluation tools were analysed and compared with their previous paper-based studies. In addition, internet impact over the last 20 years was analysed according to gender, age, nationality, and employment status. Finally, a conclusion was drawn on the impact of these tools on sustainability. The results showed that online studies have a higher number of participants (5866 users in 5 studies versus 5623 participants in 11 studies) and higher reliability rates than those of previous paper-based ones. A decrease in the social gap over the years was also found and the contribution of online tools to sustainability was discussed. This study could show the importance of the analysis of psychometric properties in online tools, the need for further research, and encourage their use in psychosocial assessment.

Graph- and Machine-Learning-Based Texture Classification

The analysis of textures is an important task in image processing and computer vision because it provides significant data for image retrieval, synthesis, segmentation, and classification. Automatic texture recognition is difficult, however, and necessitates advanced computational techniques due to the complexity and diversity of natural textures. This paper presents a method for classifying textures using graphs; specifically, natural and horizontal visibility graphs. The related image natural visibility graph (INVG) and image horizontal visibility graph (IHVG) are used to obtain features for classifying textures. These features are the clustering coefficient and the degree distribution. The suggested outcomes show that the aforementioned technique outperforms traditional ones and even comes close to matching the performance of convolutional neural networks (CNNs). Classifiers such as the support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) are utilized for the categorization. The suggested method is tested on well-known image datasets like the Brodatz texture and the Salzburg texture image (STex) datasets. The results are positive, showing the potential of graph methods for texture classification.

Additive Manufacturing of Titanium Alloys: Processability, Properties, and Applications

The restricted number of materials available for additive manufacturing (AM) technologies is a determining impediment to AM growing into sectors and providing supply chain relief. AM, in particular, is regarded as a trustworthy manufacturing technology to replace the traditional ones for Ti alloy components. Furthermore, the AM processability of Ti alloys and their features, such as microstructure, texture, and mechanical properties, is strongly dependent on the chemical composition of the processed alloy. It is essential to consider the ultimate applications as well. β Ti alloys are gaining popularity in the biomedical industry, while α + β Ti alloys are increasingly utilized for producing components in the automotive and aerospace sectors. Consequently, the topic has garnered considerable interest, and the current text reviews the advances during the last 10 years in this area to facilitate the pathway from the demanded Ti alloy to the best‐fit AM procedure. While systematically reviewing the works published in the literature, the current text attempts to establish a link between the production method, feedstock type, chemical composition, and the ultimate application. This review also features practical applications of Ti components produced by metal AM methods and offers prospective possibilities and industrial applications.

Private Equity Industry and Funding Instrument Analysis in the Post-Covid-19 Pandemic Era

A rising number of academic papers have analyzed the negative impacts of the COVID-19 epidemic on the financial markets. However, the thorough research of the pandemic's effects on financial markets and lifestyle changes remains relatively unexplored. Based on research and data from the world's major economies, including Europe and North America, this paper empirically analyses, forecasts, and assesses the worldwide private equity business and its prevalent financing techniques, as well as emerging financing methods. Due to the influence of the COVID-19 pandemic, the results indicate that some leveraged or stable companies have favorable medium-term prospects, but the long-term impact may be little. In addition, the pandemic has altered the way people live in certain regions and accelerated the growth of certain sectors, such as online education and artificial intelligence. The author also concludes that, despite the fact that the new finance methods may partially replace the traditional ones, their inadequacies are revealed.

The Valuation of Internet Companies Based on Adjusted Black -Scholes Model

With the continuous breakthrough of Internet technology, we can clearly see the huge growth space and potential profitability of related companies. However, due to their unique instant change and uncertainty, high returns and high risks exist simultaneously and finding new approaches different from traditional ones to evaluate internet companies is necessary and significant. Based on adjusted Black-Scholes Model, this study discusses three approaches: Metcalfe approach values internet companies that relies on user numbers. DCF approach and Fuzzy real option approach estimate internet companies with strong cash flow and long-period potential growth, focusing on probability and fuzzy theory separately.

Exploring Benefits and Barriers of Plant-Based Diets: Health, Environmental Impact, Food Accessibility and Acceptability.

Unhealthy dietary patterns are directly linked to the current Global Syndemic consisting of non-communicable diseases, undernutrition and climate change. The dietary shift towards healthier and more sustainable plant-based diets is essential. However, plant-based diets have wide intra differences; varying from vegan diets that totally exclude meat and animal products to traditional ones such as the Mediterranean diet and the new Nordic diet. It is acknowledged that plant-based diets may contribute simultaneously to improving population health as well as to decreasing the environmental impact of food systems. Evidence from cohort and randomized-controlled trials suggests that plant-based dietary patterns have beneficial effects on bodyweight control, cardiovascular health and diabetes prevention and treatment. On the other hand, micronutrient requirements may not be met, if some plant-based diets are not well-planned. Additionally, studies showed that lower consumption of meat and animal products results in lower environmental impacts. Consequently, plant-based diets could be a key factor to increase diet sustainability. This narrative review addresses the advantages of adherence to plant-based diets on human and planetary health considering strains and barriers to achieve this dietary transition, including cultural acceptability and affordability factors. Finally, potential intervention and policy recommendations are proposed, focusing on the update of current national food-based dietary guidelines.

SYSTEMATIC INFORMATION BASE OF ENERGY-EFFICIENT APPLICATIONS FOR AVIATION PETROL

The additives in petrol was started to use in the first half of the twentieth century. In the late 1970s, a qualitative conceptual change in the development of this area of science and practice took place. Environmental pollution from harmful emissions led to the modification of petrol formulations and structural improvements of engines. The problem of improving the quality of gasoline and fuels for jet engines is usually solved in two ways: by developing high-quality new products and by efficient use of traditional ones.
 The proposed work analyses the scientific literature on energy-efficient additives to aviation gasoline and, on this basis, creates a systematic information base, namely: classification of the range of additives to gasoline (by the following features: chemical, physical and biological stability enhancers; flash point modifiers; combustion modifiers; washing; dopes that promote efficient operation of fuels at low temperatures; friction modifiers; marking agents; anti-corrosion agents; safety enhancers; surface-active property modifiers) and the range of anti-detonation and environmental (anti-emission) additives in aviation petrols.
 The following additives are manufactured and approved for use in Ukraine: poly-functional additives representing a complex mixture of manganese compounds and mixtures of paraffinic and naphthenic hydrocarbons (TU 30218597.001[in Ukrainian] "Poly-functional additives "Eco-Octane". Technical specifications"); iron-containing multifunctional additives "Standard"; there are also known developments of oxygenates based on methanol, ethanol and other alcohols, alcohol processing products, ethers and other oxygen-containing compounds.
 The research is being carried out within the framework of the project "Development of a technology for the production of new environmentally friendly high-octane aviation gasoline from domestic raw materials" at the expense of the state budget in accordance with the Order of the Cabinet of Ministers of Ukraine of 12 August 2022 No. 713-r "Pro zatverdzhennia pereliku naukovo-tekhnichnykh (eksperymentalnykh) rozrobok v ramkakh vykonannia derzhavnoho zamovlennia na naivazhlyvishi naukovo-tekhnichni (eksperymentalni) rozrobky ta naukovo-tekhnichnu produktsiiu u 2022 rotsi" [in Ukrainian] (registration number 2020.01/0242).

Traditional and modern cartographic materials for geography teaching: From Blaž Kocen to the present

This article presents cartographic teaching materials used in two different periods: the second half of the nineteenth century and the beginning of the 2020s. During the first period examined, the works of Blaž Kocen (also Blasius Kozenn) laid the foundations of school cartography in the Habsburg Monarchy. The most highly valued among them in central Europe were his atlases, which have the longest tradition of publishing in the world. In the second period, technological development and the COVID-19 pandemic laid the foundations for a faster transition to digital approaches to teaching. This article examines the use of maps, atlases, and textbooks by Slovenian geography teachers to determine whether modern (digital) teaching materials have replaced or will replace the traditional (paper) ones. It was established that the use of printed cartographic materials continues to predominate in geography teaching, which indirectly preserves the importance of Kocen’s pioneering and visionary work.

Transition‐Metal‐Free Synthesis of Alkynylphosphine Oxides from Terminal Alkynes and Chlorodiphenylphosphine Oxide

AbstractAn efficient, LiN(SiMe3)2‐mediated phosphorylation of terminal alkynes with chlorodiphenylphosphine oxide has been developed to synthesize alkynylphosphine oxides. This transition‐metal‐free protocol was featured with simple operation, mild reaction conditions, and broad substrate scope. The tolerance of halogen and ester substituents displayed the advantages of this new method over the traditional ones. In addition, the application potential was demonstrated by the gram‐scale reaction. When using diisopropyl chlorophosphate and chlorodiphenylphosphine, the corresponding alkynylphosphate and alkynylphosphine could also been produced.

Weakly Binding Molecules‐Based Fast Charging Li‐Ion Batteries

AbstractFast charging of Li‐ion batteries (LIBs) beyond standard 0.3 C (charged in 3.3 h) are desperately pursued but hindered by sluggish desolvation kinetics of ethylene carbonate‐based traditional electrolyte, and Li‐plating and dendrites growth at graphite anode and fire hazard. Herein, a new class of weakly binding all linear molecules‐based nonflammable electrolyte (WNLE) is reported, comprising 1 m LiPF6 in ethyl methyl carbonate and 2,2,2‐trifluoroethyl acetate with additives for 10–20 times faster charging LIBs than traditional ones. The critical benefits of WNLE are 44% lower viscosity, 62% higher Li+ diffusion coefficient, 20% higher Li+ transference number, and 17% lower desolvation energy, which promotes diffusion kinetics and desolvation kinetics of Li+ in the vicinity of graphite anode enabling dendrites‐free LIB, along with nonflammability. Under 3 C (charged in 20 min), WNLE‐based industrial 800 mAh graphite//LiNi0.8Mn0.1Co0.1O2 (high active mass 13 mg cm−2) Li‐ion pouch battery achieves outstanding 700 cycles, delivering 82% capacity retention and high Coulombic efficiencies ≈100%. Robust solid electrolyte interphase layers formed at the anode and cathode mitigate interfacial failures, making fast charge to 7 C and longer cycle‐life. This new class of electrolyte formulation is a promising solution and a new opportunity to realize safe and long operation of fast‐charging LIBs for practical applications.

An intelligent wearable embedded architecture for stress detection and psychological behavior monitoring using heart rate variability

This study quantifies individual stress levels through real-time analysis of wearable sensor data. An embedded setup utilizes artificial neural networks to analyze R-R intervals and Heart Rate Variability (HRV). Emotion recognition of happiness, sadness, surprise, fear, and anger is explored using seven normalized HRV features. Statistical analysis and classification with a neural network model are performed on approximately 20,700 segments,with participants within the age ranged from 23 to 40, mixed gender, and normal health status, along with other pertinent demographics included. Findings show stress observation’s potential for mental well-being and early detection of stress-related disorders. Three classification algorithms (LVQ, BPN, CART) are evaluated, comparing ECG signal correlation features with traditional ones. BPN achieves the highest emotional recognition accuracy, surpassing LVQ by 5.9% – 8.5% and CART by 2% – 6.5%. Maximum accuracy is 82.35% for LVQ and 97.77% for BPN, but does not exceed 95%. Using only 72 feature sets yields the highest accuracy, surpassing S1 by 17.9% – 20.5% and combined S1/S2 by 11% – 12.7%. ECG signal correlation features outperform traditional features, potentially increasing emotion recognition accuracy by 25%. This study contributes to stress quantification and emotion recognition, promoting mental well-being and early stress disorder detection. The proposed embedded setup and analysis framework offer real-time monitoring and assessment of stress levels, enhancing health and wellness.

Multimodal Assessment of Cognitive Workload Using Neural, Subjective and Behavioural Measures in Smart Factory Settings.

Collaborative robots (cobots) have largely replaced conventional industrial robots in today's workplaces, particularly in manufacturing setups, due to their improved performance and intelligent design. In the framework of Industry 5.0, humans are working alongside cobots to accomplish the required level of automation. However, human-robot interaction has brought up concerns regarding human factors (HF) and ergonomics. A human worker may experience cognitive stress as a result of cobots' irresponsive nature in unpredictably occurring situations, which adversely affects productivity. Therefore, there is a necessity to measure stress to enhance a human worker's performance in a human-robot collaborative environment. In this study, factory workers' mental workload was assessed using physiological, behavioural, and subjective measures. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals were collected to acquire brain signals and track hemodynamic activity, respectively. The effect of task complexity, cobot movement speed, and cobot payload capacity on the mental stress of a human worker were observed for a task designed in the context of a smart factory. Task complexity and cobot speed proved to be more impactful. As physiological measures are unbiased and more authentic means to estimate stress, eventually they may replace the other conventional measures if they prove to correlate with the results of traditional ones. Here, regression and artificial neural networks (ANN) were utilised to determine the correlation between physiological data and subjective and behavioural measures. Regression performed better for most of the targets and the best correlation (rsq-adj = 0.654146) was achieved for predicting missed beeps, a behavioural measure, using a combination of multiple EEG and fNIRS predictors. The k-nearest neighbours (KNN) algorithm was used to evaluate the accuracy of correlation between traditional measures and physiological variables, with the highest accuracy of 77.8% achieved for missed beeps as the target. Results show that physiological measures can be more insightful and have the tendency to replace other biased parameters.