Complex Phenomenon Research Articles

Ethnic culture in urban space: a theoretical review

Relevance. Cities are centers of attraction for people of different nationalities and cultures. In the context of urbanization and globalization, various ethnic cultures interact, which leads to their transformation. The study of ethnic culture in urban space makes it possible to understand how culture changes under the influence of new living conditions, which is especially important for the formation of intercultural relations and tolerant attitude towards representatives of other cultures and nationalities.The purpose is to analyze theoretical studies of ethnic culture in urban space, including issues of tolerance, identity, and urban mentality.Objectives: to consider the elements of ethnic culture; to analyze the connection between ethnic culture and urban space; to determine how identity manifests itself in urban space; to characterize cultural ethnic practices in urban space; to characterize the visual artifacts of ethnicity in urban space.Methodology. The socio-cultural paradigm of studying urban space was used as the methodological basis of the study. The authors also applied general scientific methods: analysis, comparison, generalization.Results. A theoretical analysis of the developments of domestic authors on the topic of manifestations of ethnic culture in urban space, including aspects of national symbols, architecture, mentality, identity, tolerance, is carried out.Conclusions. Ethnic culture in urban space is a complex phenomenon encompassing socio-cultural and ethnodemographic aspects of urban community life. It finds expression in architecture, cultural practices of events, ornaments, everyday life, and the media. Today, despite the unification of everyday life and lifestyle, there are trends towards strengthening national identity, preserving national identity and cultural traditions, in particular through elements of urban public spaces.

Trafficking | Transmission | Translation: Exploring Embodiment as a Mode of Knowledge Construction in Science Art Installations

Abstract Science art practices are often framed as science communication in a manner that overlooks the capacity of the arts to provide engagement opportunities beyond discourse. The authors perform a thematic analysis of audience interviews from scientific media art installations that depict complex, intangible, invisible, and ephemeral scientific phenomena, specifically focusing on medical nanotechnology. The analysis reveals that visitors recognize sensorial experiences beyond traditional science communication, while scientists’ experiences expanded their definition of engagement. The authors argue that scientific media art practices act as sites of meaning-making and non-discursive engagement that create reflective and embodied contexts for encounters with emerging technologies.

Studying on the in vivo pathological evolution of spinal cord injury with the rat model by the method of integrated multispectral imaging and Raman spectroscopy

Spinal cord injury (SCI) is a debilitating neurological and pathological condition that results in significant impairments in motor, sensory, and autonomic functions. By integrating multispectral imaging (MSI) with Raman spectroscopy, a label-free optical methodology was developed for achieving a non-invasive in vivo understanding on the pathological features of SCI evolution. Under the guidance of captured the spectral imaging data cube with a rigid endoscope based MSI system, a special designed fiber probe passed through the instrumental channel for acquiring the finger-print spectral information from compression rat SCI models. After identifying the main visual features of injured spinal cord tissue in all Sham, 0-, 3- and 7-days post injury (0 DPI, 3 DPI, and 7 DPI) groups, the blood volume and oxygen content were visualized to describe hemorrhage, hypoxia and inflammatory state after acute injury. The averaged reflectance spectra, which were deduced from MSI data cubes, were utilized for describing oxygen saturation and hemoglobin concentration in living tissue. The results of Raman spectroscopy addressed complex compositional and conformational phenomena during SCI progression, correlated with the well-known event like neuronal apoptosis, hemorrhage, demyelination, and even the upregulation of chondroitin sulfate proteoglycans (CSPGs). A principal component analysis and linear discriminate algorithm (PCA-LDA) based discriminate model was introduced for categorizing spectral features in different injury stages, which was applicable for intraoperative interpretations on the complex pathological courses of SCI and therapeutic outcomes.

Enhancing experimental image quality in two-phase bubbly systems with super-resolution using generative adversarial networks

Fluid Dynamics is a key scientific field to multitudes of engineering applications. Experimental work in this field requires careful set-up and expensive image-capturing equipment, particularly when considering the finer details of complex phenomena. In this work, we study the application of super-resolution Generative Adversarial Networks (GANs) to achieve high-resolution results by upscaling lower-resolution experimental images.We train GANs proposed for natural images on a bubbly flow experimental Fluid Dynamics dataset and compare common super-resolution evaluation metrics to domain expert assessments of the upscaled images. We find that these models achieve promising results, as evaluated by experts, and transfer learning from natural images translates to better performance overall. Attention mechanisms are found to be particularly useful in recreating sharper details. On the other hand, traditional super-resolution evaluation metrics are found to align poorly with expert perception of quality, signaling the need for better systematic evaluation methodologies in this domain.

The experience of adolescence process among French teenager pregnancies: a mixed-methods study

ABSTRACT Purpose Teenage pregnancy remains a significant global public health concern worldwide. However, it presents a complex phenomenon in developed countries, carrying potential short- and long-term consequences for both mothers and children. Methods This mixed method study used data from the French cross-sectional study “Portraits of adolescents”, which included 6000 girls aged between 13 and 17 years. The quantitative approach involved comparisons between a subgroup with an history of pregnancy and their peers, examining their lived-experience and mental health. The qualitative approach investigated the question “What does being a teenager mean for you?” specifically for the girls who reported an history of pregnancy. Results Teenage pregnancies presented elevated rates of mental health disorders, including dark thoughts, depression, self-harm, participating in dangerous games, attempting suicide and increased use of psychoactive substances. With limited support, in comparison to their peers. The qualitative approach revealed three major themes: “being in action”, “a way of feeling”, and “quality of relationship”. Conclusion This vulnerable subgroup of adolescents suggests the need for a coordinated multidisciplinary healthcare approach, given their limited parental and friend support, with a high risk of experiencing poor mental health. Additionally, these findings portray a “silent sufferer” population characterized by difficulties recognizing or managing emotions due to difficulties in expressing their emotional distress.

Hyper-local black carbon prediction by integrating land use variables with explainable machine learning model

This research introduces an innovative framework that effectively integrates the Land Use Regression (LUR) model with an explainable machine learning methodology, thereby enhancing the causal understanding of how land use variables impact Black Carbon (BC) concentrations in complex urban landscapes. Through the development and application of a LUR-based Bayesian Network (BN) model to detailed, mobile-based BC measurements, we have successfully decoded the causal relationships and conditional probabilities linking diverse urban land use variables to BC levels. The BN model demonstrates notable prediction accuracy on the test set, with an R2 of 0.59 and an NRMSE of 0.65. Among the varied land use variables analyzed, proximity to the port is identified as the dominant factor contributing to BC hotspots. Moreover, the synergistic effect of port proximity and truck routes emerges as a key driver of spatially clustered BC hotspots, a complex phenomenon not adequately captured by conventional machine learning and deep learning models. The insights revealed through this study are vital not just for enhancing air pollution prediction and exposure analysis but also for informing urban planning and policymaking, offering valuable, actionable insights.

Prediction of runoff characteristics in permeable pavements using experimental data and intelligent models.

Considering the growing use of permeable pavements, the prediction of runoff passing through this pavement model is of considerable importance. The prediction of rainfall-runoff relationships can be a challenge because of several factors including data uncertainty, non-linear relationships, and high temporal and spatial variability. To deal with these challenges, intelligent algorithms are often used to predict such complex phenomena. In this research, runoff control parameters were investigated in two types of permeable pavements (permeable interlocking concrete pavement (PICP) and high strength clogging resistant permeable pavement (CRP)) using support vector machine (SVM), support vector machine-bat (SVM-BA) and support vector machine-grasshopper (SVM-GOA). Variables used in the models included percentage of coverage by permeable pavement (A), rainfall intensity (I), slope (S), and pavement type coefficient (K) as input data, and runoff coefficient (C), time to runoff (Tr), and peak discharge (Qp) as output data. In this research, from the total of 108 data extracted from the experimental results, 86 data were used in the training period, and 22 data were used in the test period. The results of the test period show that the SVM-BA model has the best performance with values of MAE = 0.010 in predicting C, MAE = 1.330 min in predicting Tr, and MAE = 0.029 lit/min in predicting Qp. The SVM-GOA model is ranked second with values of MAE = 0.051 in predicting C, MAE = 3.285 min in predicting Tr, and MAE = 0.097 lit/min in predicting Qp. Also, the SVM model is ranked third with values of MAE = 0.063 in predicting C, MAE = 4.470 min in predicting Tr, and MAE = 0.121 lit/min in predicting Qp. In summary, the SVM-BA algorithm showed the best performance and the SVM algorithm showed the weakest performance in predicting runoff characteristics in permeable pavements.

Numerical simulation of Alfvén dark envelope soliton in Hall-MHD plasmas

This study investigates the propagation of dark Alfvén solitons in low-β magnetized plasma using Hall-magnetohydrodynamic (Hall-MHD) numerical simulations. The rational solution of the nonlinear Schrödinger equation (NLSE) is presented, which is proposed as an effective tool for studying dark envelope solitons in plasma. Our results show a high degree of agreement between numerical simulations and analytical solutions derived from the NLSE via the reductive perturbation method. This agreement validates our modeling and computational approach. In addition, the simulations confirm the existence of dark Alfvén solitons in magnetized plasma. This work demonstrates the effectiveness of Hall-MHD simulations in studying complex plasma phenomena, contributing to the broader understanding of soliton propagation in plasma environments.

Цифровая платформа в сфере судебной деятельности: понятие, признаки, обеспечение надлежащего функционирования

The study examines the phenomenon of digital platforms in judicial activity. Based on the characteristics of digital platforms, the author proposes to define a digital justice platform as a complex legal phenomenon. The article examines the features of judicial management in the context of platformatization, suggests the levels and subjects of management of the digital justice platform.

Comparative morphology in the context of facial reduction: Modularity in primate, dog, and bat crania.

Biological variation in the mammalian skull is the product of a series of factors including changes in gene expression, developmental timing, and environmental pressures. When considering the diversity of extant mammalian crania, it is important to understand these mechanisms that contribute to cranial growth and in turn, how differences in cranial morphology have been attained. Various researchers, including Dr. Sue Herring, have proposed a variety of mechanisms to explain the process of cranial growth. This work has set the foundation on which modern analysis of craniofacial morphology happens today. This study focused on the analysis of modularity in three mammalian taxa, all of which exhibit facial reduction. Specifically, we examined facial reduction as a morphological phenomenon through the use of two-module and six-module modularity hypotheses. We recorded three-dimensional coordinate data for 55 cranial landmarks that allowed us to analyze differences in cranial shape in these three taxa (primates n = 88, bats n = 64, dogs n = 81). When assessing modularity within the two-module modularity hypothesis specifically, dogs exhibited the lowest levels of modularity, while bats and primates both showed a slightly more modular covariance structure. We further assessed modularity in the same sample using the Goswami six-module model, where again dogs exhibited a low degree of modularity, with bats and primates being more moderate. We then broke the sample into subsets by analyzing each morphotype separately. We hypothesized that the modularity would be more pronounced in the brachycephalic morphotype. Surprisingly, we found that in brachycephalic dogs, normocephalic dogs, brachycephalic primates, and normocephalic primates, there was a moderate degree of modularity. Brachycephalic bats had a low degree of modularity, while normocephalic bats were the most modular group observed in this study. Based on these results, it is evident that facial reduction is a complex and multifaceted phenomenon with unique morphological changes observed in each of the three taxa studied.

Effect of nonlinear terms in piston theory on characteristics of panel flutter

Currently, there is no systematic summary of the influence of the nonlinear terms in piston theory on the panel flutter. The two-dimensional nonlinear panel flutter equations under supersonic airflow based on the first, second, and third-order piston theories is established. The stability of the heated panel is analyzed by using the Lyapunov's indirect method, and the panel flutter equations are numerically solved based on the numerical analysis method to investigate the influence of the nonlinear terms in piston theory on the panel flutter. The results show that: ①Under a small temperature rise ratio, the panel response under the first-order piston theory only exhibits the convergent motion and single-period limit cycle flutter. While under higher-order piston theories, the panel response is more complex, exhibiting more complex nonlinear dynamic phenomena such as multi-period limit cycle flutter and chaotic motion in addition to the aforementioned characteristics. ②As the Mach number increases, the required dynamic pressure and temperature rise ratio decrease gradually when the dynamic response of the panel under the first-order piston theory and higher-order piston theories exhibit significant differences. ③The significant differences in the computational results of the second-order and third-order piston theory appear under the high Mach numbers and relatively high dynamic pressures, and the same phenomenon occurs underthe high temperature rise ratios. ④When the dynamic response characteristics of the panel are basically consistent, the displacement response peak calculated by using the higher-order piston theory is usually smaller than the result calculated by using the first-order piston theory, and the maximum error can reach about 16.66%. The present results have certain reference value for selecting the appropriate analysis method for the panel flutter under different conditions in practical applications.

Soil resilience assessment using soil profile descriptions and Analytic Hierarchy Process in Mediterranean mountains considering diverse fire occurrences

Wildfires are complex natural phenomena that exert significant impacts on landscapes, societies, and economies. Understanding the concept of resilience is crucial in mitigating its possible negative impacts, as it involves preparing for, responding to, and recovering from wildfires. This research aims to demonstrate the utility of in situ soil profile description in assessing land use resilience using an Analytic Hierarchy Process (AHP) through an expert panel survey. The study examines a catchment located in the Balearic Islands, considering two fire occurrences (once and twice), comparing abandoned agricultural terraces and natural hillslopes. The results demonstrated that the priority ranking of variables to assess soil profile resilience against wildfires, determined by a panel of 10 experts, identified horizon depth (25.1%), slope inclination (21.5%), and hydrological connectivity (16.6%) as the most crucial factors. Other variables, such as number and size of roots, structure of pedal soil material, size class structure, and rock fragments, also contributed to resilience but to a lesser extent, with scores ranging from 5.7% to 9.6%. Analyzing the priorities established by the experts using AHP, the results showed that the least resilient soil horizon was H1 of the control hillslope, especially under high and low connectivity processes, which aligned with the loss of superficial soil horizons after one and two wildfires. Hillslopes showed greater changes in resilience after occurring wildfires compared to terraces, with the most significant alterations occurring after the second wildfire event. This study addresses a significant knowledge gap in the field by highlighting the interconnectedness of wildfires, resilience, and land use, providing insights into land management strategies for wildfire-prone regions.

Phylogenomics of the pantropical Connaraceae: revised infrafamilial classification and the evolution of heterostyly

Connaraceae is a pantropical family of about 200 species containing lianas and small trees with remarkably diverse floral polymorphisms, including distyly, tristyly, homostyly, and dioecy. To date, relationships within the family have not been investigated using a targeted molecular phylogenetic treatment, severely limiting systematic understanding and reconstruction of trait evolution. Accordingly, their last infrafamilial classification was based only on morphological data. Here, we used phylogenomic data obtained using the Angiosperms353 nuclear target sequence capture probes, sampling all tribes and almost all genera, entirely from herbarium specimens, to revise infrafamilial classification and investigate the evolution of heterostyly. The backbone of the resulting molecular phylogenetic tree is almost entirely resolved. Connaraceae consists of two clades, one containing only the African genus Manotes (4 or 5 species), which we newly recognize at the subfamily level. Vegetative and reproductive synapomorphies are proposed for Manotoideae. Within Connaroideae, Connareae is expanded to include the former Jollydoreae. The backbone of Cnestideae, which contains more than half of the Connaraceae species, remains incompletely resolved. Reconstructions of reproductive system evolution are presented that tentatively support tristyly as the ancestral state for the family, with multiple parallel losses, in agreement with previous hypotheses, plus possible re-gains. However, the great diversity of stylar polymorphisms and their phylogenetic lability preclude a definitive answer. Overall, this study reinforces the usefulness of herbarium phylogenomics, and unlocks the reproductive diversity of Connaraceae as a model system for the evolution of complex biological phenomena.

Couple & Family Quality of Life When There Is a Child With a Chronic Behavioral Disability

Abstract Date Presented 03/22/24 Family quality of life is a complex phenomenon made ever more complicated when raising a child with a behavioral disorder. To provide effective interventions for children with behavioral disorders, practitioners should consider the entire family. Primary Author and Speaker: Kathleen Eglseder Additional Authors and Speakers: Barbara Demchick

Exact solutions of non-singularized MHD Casson fluid with ramped conditions: A comparative study

The study of magnetohydrodynamic (MHD) fluids has significant implications across various scientific disciplines, particularly in understanding complex phenomena in astrophysics, engineering, and geophysics. The Casson fluid model stands as a crucial tool for describing non-Newtonian behaviors observed in certain fluid systems. The current work shows an analytical analysis to determine the ramped effect on the fractionalized MHD Casson fluid over an vertical plate. Fractional partial differential equations are used to formulate the problem along with initial and boundary conditions. The governing equations are transformed into the dimensionless form and developed fractional models like Caputo-Fabrizio and Atangana-Baleanu Derivative. We used the Laplace transform technique to find the closed form solution of the dimensionless governing equation analytically. MATHCAD software is being used for numerical computations and the physical attributes of material and fractional parameters are discussed. To analyze their behavior clearly, two-dimensional graphical results are plotted for velocity profile and temperature as well. It has been concluded that the fluid’s velocity are reduced for larger values of the fractional parameter and Prandtl number and is maximum for small values of both parameters.

Pharmacochemical Study of Multitarget Amino Acids' Hybrids: Design, Synthesis, In vitro, and In silico Studies.

Neuro-inflammation is a complex phenomenon resulting in several disorders. ALOX-5, COX-2, pro-inflammatory enzymes, and amino acid neurotransmitters are tightly correlated to neuro-inflammatory pathologies. Developing drugs that interfere with these targets will offer treatment for various diseases. Herein, we extend our previous research by synthesizing a series of multitarget hybrids of cinnamic acids with amino acids recognized as neurotransmitters. The synthesis was based on an In silico study of a library of cinnamic amide hybrids with glycine, γ- aminobutyric, and L - glutamic acids. Drug-likeness and ADMET properties were subjected to In silico analysis. Cinnamic acids were derived from the corresponding aldehydes by Knoevenagel condensation. The synthesis of the amides followed a two-step reaction with 1- hydroxybenzotriazole monohydrate and 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride in dry dichloromethane and the corresponding amino acid ester hydrochloride salt in the presence of N,N,-diisopropyl-Nethylamine. The structure of the synthesized compounds was confirmed spectrophotometrically. The new compounds, such as lipoxygenase, cyclooxygenase-2, lipid peroxidation inhibitors, and antiinflammatories, were tested in vitro. The compounds exhibited LOX inhibition with IC50 values in the low μM region). Compounds 18a, 23b, and 11c are strong lipid peroxidation inhibitors (99%, 78%, and 92%). Compound 28c inhibits SLOX-1 with IC50 =8.5 μM whereas 11a and 22a highly inhibit COX-2 (IC50 6 and 5 μM Hybrids 14c and 17c inhibit both enzymes. Compound 29c showed the highest anti-inflammatory activity (75%). The In silico ADMET properties of 14c and 11a support their drug-likeness.

Comparing parameter-reduction methods on a biophysical model of an auditory hair cell

Biophysical models describing complex cellular phenomena typically include systems of nonlinear differential equations with many free parameters. While experimental measurements can fix some parameters, those describing internal cellular processes frequently remain inaccessible. Hence, a proliferation of free parameters risks overfitting the data, limiting the model's predictive power. In this study, we develop systematic methods, applying statistical analysis and dynamical-systems theory, to reduce parameter count in a biophysical model. We demonstrate our techniques on a five-variable computational model designed to describe active, mechanical motility of auditory hair cells. Specifically, we use two statistical measures, the total-effect and PAWN indices, to rank each free parameter by its influence on selected, core properties of the model. With the resulting ranking, we fix most of the less influential parameters, yielding a five-parameter model with refined predictive power. We validate the theoretical model with experimental recordings of active hair-bundle motility, specifically by using Akaike and Bayesian information criteria after obtaining maximum-likelihood fits. As a result, we determine the system's most influential parameters, which illuminate the key biophysical elements of the cell's overall features. Even though we demonstrate with a concrete example, our techniques provide a general framework, applicable to other biophysical systems. Published by the American Physical Society 2024

CHILD MIGRATION TRENDS IN INDIA: INSIGHTS FROM NATIONAL DATASETS

The child migration in India is a complex socio-economic phenomenon profoundly impacting childrens well-being and development. This paper analyzes trends and vulnerabilities among child migrants using national datasets, focusing on education, health, and socio-economic disparities.The study identifies a significant increase in internal migration, predominantly rural, driven by familial and economic factors. Gender disparities show higher migration rates among boys, influenced by socio-cultural norms and economic incentives. The regional disparities highlight Goa, Kerala, and Maharashtra as key migration hubs, shaped by economic opportunities and cultural dynamics. Despite higher literacy rates, migrant children face challenges like early employment and educational disruptions. Health outcomes vary, with improved nutrition but lower immunization coverage compared to non-migrant peers.Child marriage rates among migrant girls underscore socio-economic inequities. Vulnerability analysis reveals risks among economically disadvantaged and rural migrants, necessitating targeted policy interventions. The policy recommendations stress legal enforcement against child marriage and robust implementation of education and child labor laws. Collaborative efforts involving government, NGOs, and communities are crucial to address the needs of child migrants, especially from marginalized groups.This research paper informs inclusive policies to protect child migrants rights and opportunities, aiming for a more equitable societal framework.

Meaningful body talk: Emotional experiences with music-based group interactions

Music-based group interaction (MGI) is a complex social phenomenon that is important for individuals' emotional experiences. However, research on emotion in tourism lacks a focus on group interaction integrating the mind and body. Guided by Damasio's neurocognitive-evolutionary theory, this study used an adapted go-along method at Midi Festivals to analyze festivalgoers' emotional experiences during MGI. Meanings of the body and its agency-related roles were captured between emotions, music, and group interaction. Results revealed three patterns of MGI—dissociative, reflective, and immersive—based on the extent of congruence between social and bodily rhythms. MGI allows participants to achieve a clear sense of self and shared belonging thanks to the unity between body and mind. The findings provide insights into contemplating emotions, the body, and group interaction within a musical context. Results suggest that music tourism holds value for enhancing individuals' emotion regulation and well-being, offering implications for practitioners in event management.

PENDEKATAN HOLISTIK DALAM HAK ASUH ANAK PASCA PERCERAIAN: PERSPEKTIF MAQĀṢID SYARĪ’AH AL-TAHIR IBNU ASYUR

Divorce is a complex social phenomenon with significant impacts on various aspects of life, particularly concerning child custody issues. Given that child custody has long been a topic of special interest among Islamic scholars and intellectuals, this research aims to explore how a holistic approach to child custody after divorce can be formulated and implemented using Ibn Ashur's perspective of maqāṣid syarī’ah (objectives of Islamic law). This study is normative legal research utilizing a conceptual approach, with Miles and Huberman's analysis theory applied as the analytical technique. The findings of this study reveal that Ibn Ashur expanded the concept of maqāṣid syarī’ah by emphasizing the development and enhancement of human quality of life, beyond the traditional focus on mere protection. In the context of child custody after divorce, Islam emphasizes the child's well-being as the primary concern. The concept of hadhanah encompasses the fulfillment of the child's physical, emotional, and spiritual needs, with shared responsibility between both parents, even after divorce. The holistic approach to child custody inspired by Ibn Ashur's thought emphasizes the importance of considering the child's natural disposition (fitrah), flexibility in arrangements, maslahah (public interest) as the main principle, equality in parental roles, and respect for the child's freedom to express their opinions.