Individual States Research Articles

Studying Transient Phenomena in Thin Films with Reinforcement Learning.

Neutron reflectometry has long been a powerful tool to study the interfacial properties of energy materials. Recently, time-resolved neutron reflectometry has been used to better understand transient phenomena in electrochemical systems. Those measurements often comprise a large number of reflectivity curves acquired over a narrow q range, with each individual curve having lower information content compared to a typical steady-state measurement. In this work, we present an approach that leverages existing reinforcement learning tools to model time-resolved data to extract the time evolution of structure parameters. By mapping the reflectivity curves taken at different times as individual states, we use the Soft Actor-Critic algorithm to optimize the time series of structure parameters that best represent the evolution of an electrochemical system. We show that this approach constitutes an elegant solution to the modeling of time-resolved neutron reflectometry data.

Trends, main economic determinants of the e-mobility in the EU: Additional evidence and verification

The adoption of e-mobility in the countries of the European Union shows significant differences in terms of dynamics of electric car sales and in terms of differences in the share of electric cars in the total fleet of passenger cars in individual Member states. Despite the common European policy of transition to less emission-intensive car drives, the economic performance of countries, as well as other determinants, turn out to be the key drivers of differences in the sales and shares of electric cars on the car fleet within individual countries. In light of these findings, the paper contributes to the existing discussion and knowledge with additional recent findings and how intensive main consensual factors cause differences in the recent electric cars market shares in the EU countries.

Weight loss impacts risky decisions in obesity

Risky decision making is shaped by individual psychological and metabolic state. Individuals with obesity show not only altered risk behavior, but also metabolic and psychological abnormalities. The aim of the present study was to investigate whether a substantial weight loss in individuals with severe obesity will 1) normalize their metabolic and psychological state and 2) will change their pattern of decision guidance. We assessed the effect of glycated hemoglobin (HbA1c) and mood on risk behavior in individuals with obesity (n=62, 41 women; BMI, 46.5±4.8kg/m2; age, 44.9±14.7 years) before and after 10-weeks weight loss intervention. Results showed that this intervention reduced participants' risk behavior, which was significantly predicted by their changes in BMI. Before intervention, mood, but not HbA1c significantly predicted decisions. After the weight loss, mood no longer, but HbA1c significantly predicted decisions. Our findings shed light on the psychological and metabolic mechanisms underlying altered risky decision making in severe obesity and can inform the development of strategies in the context of weight loss interventions.

Association between mild depressive states in polycystic ovary syndrome and an unhealthy lifestyle.

Polycystic Ovary Syndrome (PCOS) is a prevalent and frequently encountered gynecological disorder. Its high variability and the complexities associated with its management often lead to psychological stress in affected women, manifesting in symptoms of depression. Embracing a healthy lifestyle is fundamental in PCOS treatment. Consistent adherence to a healthy lifestyle not only aids in improving PCOS symptoms but also plays a role in enhancing mental well-being. However, there is currently limited research examining the extent of depression, its prevalence, and its correlation with lifestyle among individuals with PCOS. Therefore, this study aims to explore the impact of lifestyle factors on the depressive state of individuals with PCOS. This cross-sectional study gathered data from 411 individuals with PCOS at a comprehensive hospital in Henan, China. Depression status was assessed using the Hamilton Depression Scale, and demographic information as well as lifestyle habits were simultaneously collected. Univariate and multivariate analyses using logistic regression were conducted to identify risk factors associated with the depressive state in PCOS. Among the surveyed 411 individuals with PCOS, approximately 49.4% exhibited symptoms of depression, with 83.7% experiencing mild depressive symptoms. A disease duration of 1-3 years, the presence of acne, and unhealthy lifestyle factors such as high-fat diet, staying up late, lack of exercise, and mental stress emerged as significant risk factors for the onset of depressive symptoms. Depressive symptoms in individuals with PCOS are predominantly mild. The risk of comorbid depression in PCOS is associated with the presence of acne, frequent high-fat diet, regular staying up late, lack of exercise, and mental stress.

Gender stereotypes in family relationships in the conditions of modern Kyrgyzstan: Psychological, religious, socio-philosophical, cultural (spiritual) aspects

Relevance. Gender stereotypes significantly affect the psychological state of individuals in the family, shaping their perceptions of themselves and their roles in relationships. This makes the study of these stereotypes essential and relevant. Purpose. This paper aims to investigate the dynamics and nature of gender stereotypes in family relations within the context of 21st-century Kyrgyz society. Methodology. The study employs methods of abstraction, analysis, systematization, and generalization. Results. The findings indicate that gender stereotypes continue to influence family dynamics in Kyrgyzstan. These stereotypes are formed and sustained by various sociocultural institutions, including the family, schools, and religious communities. The study reveals that gender stereotypes in the family can lead to conflicts and hinder personal development, limiting the opportunities of each family member. Conclusions. Overcoming these stereotypes requires a comprehensive approach, including educational programs, awareness-raising initiatives, and support from the State. Examples of equal family roles, cultural and educational activities, and the promotion of economic self-sufficiency and active youth involvement are critical factors. Additionally, globalization and the media significantly influence the formation of gender stereotypes but can also aid in their revision and eradication by supporting diverse and inclusive representations of gender roles in society. The findings provide a foundation for developing strategies and programs to address gender stereotypes in family relationships. Keywords: gender stereotypes; Kyrgyzstan; religious factor; discrimination against women

Contribution of magnons created in volume and within the surface in low-dimensional dilute magnetic semiconductor: eTBA Calculation for [formula omitted

The magnetic properties in low-dimensional dilute magnetic semiconductor system (Sn1−xCoxO2) are studied in the framework of extended Tight Binding Approximation (eTBA). According to the parameters of the solid, we established the Schrödinger equation to a single spin-wave state constructed as a sum of Bloch of atomic-type individual states of creations of spin deviations on a given site. We developed a tight binding interatomic potential that essentially represents the combined effect of direct exchange J and the magneto-crystalline anisotropy Δ. We demonstrated the existence of two kinds of magnons created in volume and in the surface of the Sn1−xCoxO2/SiO2 multilayers. We calculated the excitation spectrum of these two kinds of magnons. The magnetization per spin is also obtained. The calculated values are in very satisfactory agreement with the experimental measurements.

An examination of state and trait urgency in individuals with binge-eating disorder.

Negative urgency (i.e., acting rashly when experiencing negative affect; NU), is a theorised maintenance factor in binge-eating type eating disorders. This study examined the association between trait NU and eating disorder severity, momentary changes in state NU surrounding episodes of binge eating, and the momentary mechanistic link between affect, rash action, and binge-eating risk. Participants were 112 individuals with binge-eating disorder (BED). Baseline measures included the UPPS-P Impulsive Behaviour Scale to assess trait NU and the Eating Disorders Examination to assess binge-eating frequency and global eating disorder severity. Ecological momentary assessment captured real-time data on binge eating, negative affect, and state NU. Multiple regression analysis revealed a strong association between trait NU and eating disorder severity. Generalised estimating equations showed that state NU increased before and decreased after binge-eating episodes, and that this pattern was not moderated by trait-level NU. Finally, a multilevel structural equation model indicated that increases in rash action mediated the momentary relationship between states of high negative affect and episodes of binge eating. These findings underscore the importance of both trait and state NU in binge-eating type eating disorders, and suggest NU as a potential key target for intervention.

Bayesian Mechanistic Inference, Statistical Mechanics, and a New Era for Monte Carlo.

On the one hand, much of computational chemistry is concerned with "bottom-up" calculations which elucidate observable behavior starting from exact or approximated physical laws, a paradigm exemplified by typical quantum mechanical calculations and molecular dynamics simulations. On the other hand, "top down" computations aiming to formulate mathematical models consistent with observed data, e.g., parametrizing force fields, binding or kinetic models, have been of interest for decades but recently have grown in sophistication with the use of Bayesian inference (BI). Standard BI provides an estimation of parameter values, uncertainties, and correlations among parameters. Used for "model selection," BI can also distinguish between model structures such as the presence or absence of individual states and transitions. Fortunately for physical scientists, BI can be formulated within a statistical mechanics framework, and indeed, BI has led to a resurgence of interest in Monte Carlo (MC) algorithms, many of which have been directly adapted from or inspired by physical strategies. Certain MC algorithms─notably procedures using an "infinite temperature" reference state─can be successful in a 5-20 parameter BI context which would be unworkable in molecular spaces of 103 coordinates and more. This Review provides a pedagogical introduction to BI and reviews key aspects of BI through a physical lens, setting the computations in terms of energy landscapes and free energy calculations and describing promising sampling algorithms. Statistical mechanics and basic probability theory also provide a reference for understanding intrinsic limitations of Bayesian inference with regard to model selection and the choice of priors.

Autonomy and identity: the role of two developmental tasks on adolescent's wellbeing.

Developmental tasks theory suggests that the wellbeing of adolescents can be better understood by considering their developmental tasks, rather than just viewing it as a specific age. This concept has significantly influenced contemporary studies on the transition to adulthood. Study explains that psychosocial developmental tasks involve shifts in an individual's psychological state and social relationships, potentially disrupting their previous equilibrium. Authors stress the importance of forming personal identity and fostering a healthy sense of independence, especially concerning identity and autonomy tasks, as crucial elements for adolescent wellbeing analysis. In line with this, the study focuses on the concurrent and predictive relationship between identity process of commitment, emotional autonomy, and psychological wellbeing among adolescents. Four hundred fifty-four (454) participants with an age ranged from 15 to 19 years (m = 17.96) completed surveys measuring emotional autonomy, identity commitment, and psychological wellbeing. The findings revealed that both emotional autonomy and identity commitment predicted psychological wellbeing, but in different ways. The study revealed that emotional autonomy is inversely associated with psychological wellbeing in middle and late adolescents, indicating that their ability to establish independence from their parents or relinquish unrealistic parental expectations may be linked to a decline in their mental health. The results also indicated that identity commitment is moderately and positively related to psychological wellbeing, suggesting that when adolescents have a clear sense of their identity, they may experience greater wellbeing. This finding underscores the importance of encouraging adolescents to explore their values, interests, and goals, as well as providing them with support and guidance throughout the process. Additionally, the findings of the study revealed that emotional autonomy has a negative influence on psychological wellbeing when identity commitment is low or average. Specifically, when individuals have a low level of identity commitment, emotional autonomy significantly predicts lower psychological wellbeing. Similarly, when identity commitment is at an average level, emotional autonomy also has a detrimental effect on psychological wellbeing. Lastly, the study revealed that when individuals have a high level of identity commitment, emotional autonomy does not have a significant impact on psychological wellbeing. In simpler terms, when individuals possess a strong sense of commitment to their identity, their level of emotional autonomy does not play a significant role in influencing their psychological wellbeing.

Hepatitis B virus-specific human stem cell memory T cells differentiate into cytotoxic T cells and eradicate HBV-infected hepatocytes in mice.

Chronic infection with the hepatitis B virus (HBV) induces progressive hepatic impairment. Achieving complete eradication of the virus remains a formidable challenge. Cytotoxic T lymphocytes, specific to viral antigens, either exhibit a numerical deficiency or succumb to an exhausted state in individuals chronically afflicted with HBV. The comprehension of the genesis and dissemination of stem cell memory T cells (TSCMs) targeting HBV remains inadequately elucidated. We identified TSCMs in subjects with chronic HBV infection and scrutinized their efficacy in a murine model with human hepatocyte transplants, specifically the TK-NOG mice. TSCMs were discerned in all subjects under examination. Introduction of TSCMs into the HBV mouse model precipitated a severe necro-inflammatory response, resulting in the elimination of human hepatocytes. TSCMs may constitute a valuable tool in the pursuit of a remedial therapy for HBV infection.

Real Time Depression and Anxiety Detection Using Machine Learning

Depression affects a staggering 264 million individuals worldwide, constituting a significant cause of disability. The detrimental impact of a negative workplace environment extends beyond productivity loss to encompass physical and medical ailments. Unfortunately, individuals often refrain from seeking help due to the pervasive stigma surrounding mental health issues. Leveraging the potential of machine learning, we have embarked on a journey to predict depression using diverse algorithms. Our study draws upon routine survey data, delving into factors such as home and workplace environments, family history of mental illness, among others. Recognizing and understanding the mental state of individuals, be it stress, anxiety, or depression, holds paramount importance in averting untoward incidents. Recent events, such as economic downturns, pandemic-induced fears, and social isolation, have contributed to a surge in depression and anxiety cases. Furthermore, there is compelling evidence indicating heightened social media usage among individuals with mental health disorders. Thus, we delve into the potential of online personas on social media platforms. Our work presents a comprehensive review of various methodologies employed in the literature for detecting depression, thereby shedding light on emerging trends and challenges. By identifying gaps in existing research, we aim to provide fresh insights and directions for researchers committed to advancing the field of depression detection. Key Words: Depression Detection, Machine Learning, Support Vector Machine (SVM), Accuracies, Human Being.

Features of the Implementation of Sustainable Development Goals in the Regional Integration Systems of the EAEU Countries

Aim. To study the features of strategic planning and practical implementation of the concept of sustainable development of the countries of the Eurasian Economic Union, taking into account the Sustainable Development Goals in the context of the formation of a new architecture of the world structure and a change in the current world order.Tasks. Consider the essence and principles of the SDG implementation; identify the specifics of the integration of the SDGs into the national strategies of individual states of the post-Soviet space; analyze the features of the localization of SDGs at the regional level using the example of Eurasian integration; determine the role of the EAEU in the context of the formation of a multipolar world system and strengthening the its position as a macro-regional center of the power; justify the priorities for achieving the SDGs in the context of sustainable development of the EAEU.Methods. To achieve the set goals the following methodological approaches were used in the article. The constructivist approach makes it possible to determine the essence of sustainable development, its significance for society and the economy, as well as to analyze the regulatory and legal framework of the EAEU countries in this area. Comparative analysis made it possible to determine the relationship of SDGs with changed world architectures in retrospect and the current conditions. Empirical analysis makes it possible to assess the practical steps and efforts of the EAEU countries to achieve the SDGs, including the implementation of innovative projects and the rational use of the resource base.Results. Understanding and regulation of sustainable development in the EAEU is justified. The study made it possible to establish that the countries of the Union attach importance to sustainable development, but at the legislative level there remains a problem of insufficient consistency of regulatory legal acts on this topic. This highlights the need for a clearer and more concrete regulatory framework for sustainable development. The study allowed to analyze the impact of globalization on the world and regional structure. It is argued, that the EAEU, as a regional association, has a unique opportunity to gain a foothold as one of the macro-regions in the multipolar world system. This provides strategic advantages in the context of a change in the world order on the way to multipolarity.Conclusions. The study identified a number of priority tasks contributing to the achievement of the SDGs in the context of the sustainable development of the EAEU. The most important factors in this context are: food independence, energy sustainability, strengthening economic integration, infrastructure development, focus on hard innovation and digitalization.

Technologization of Legal Solutions in the field of International Trade Compliance

Modern technologies, and above all — information technologies, do play a significant role in navigating the complex regulatory and organizational landscape of compliance with international trade rules and management in the international trade system. The technologization of international trade compliance programs, internal compliance programs, the processes of systematization and automation of work with databases should be developed and implemented in all organizations engaged in international trade as an integral part of doing business and systematization of international trade operations. Taking into account the nature of international trade operations and their even minimal connection with specific States, in addition to clearly developing programs for compliance with international standards of trade and customs operations, the author substantiates a differentiated approach to international trade compliance, involving consideration of regulatory requirements of individual States and separate monitoring of compliance with the requirements of the State with which the relationship is associated. The author also dwells on a differentiated substantive approach to compliance procedures in trade using various software tools for monitoring and ensuring compliance in the absence, in most cases, of detailed international legal regulation (based on international treaties) of export-import operations.

Exploring contactless techniques in multimodal emotion recognition: insights into diverse applications, challenges, solutions, and prospects

In recent years, emotion recognition has received significant attention, presenting a plethora of opportunities for application in diverse fields such as human–computer interaction, psychology, and neuroscience, to name a few. Although unimodal emotion recognition methods offer certain benefits, they have limited ability to encompass the full spectrum of human emotional expression. In contrast, Multimodal Emotion Recognition (MER) delivers a more holistic and detailed insight into an individual's emotional state. However, existing multimodal data collection approaches utilizing contact-based devices hinder the effective deployment of this technology. We address this issue by examining the potential of contactless data collection techniques for MER. In our tertiary review study, we highlight the unaddressed gaps in the existing body of literature on MER. Through our rigorous analysis of MER studies, we identify the modalities, specific cues, open datasets with contactless cues, and unique modality combinations. This further leads us to the formulation of a comparative schema for mapping the MER requirements of a given scenario to a specific modality combination. Subsequently, we discuss the implementation of Contactless Multimodal Emotion Recognition (CMER) systems in diverse use cases with the help of the comparative schema which serves as an evaluation blueprint. Furthermore, this paper also explores ethical and privacy considerations concerning the employment of contactless MER and proposes the key principles for addressing ethical and privacy concerns. The paper further investigates the current challenges and future prospects in the field, offering recommendations for future research and development in CMER. Our study serves as a resource for researchers and practitioners in the field of emotion recognition, as well as those intrigued by the broader outcomes of this rapidly progressing technology.

Abstract LB291: High-plexy intracellular and surface CITE-Seq offers multiomic insights into cell function

Abstract CITE-Seq allows researchers to combine high-plex protein information with whole transcriptome sequencing to gain insights about individual cell states. Due to the harsh fixation and permeabilization required for intracellular (IC) antibody staining, only a few studies have recovered mRNA expression profiles alongside IC protein detection. This experiment utilized a novel IC CITE-Seq assay that enables robust profiling of multiple IC protein targets in combination with mRNA and high-plex surface proteins. In this study, we stimulated human peripheral blood mononuclear cells (PBMCs) alongside a resting control. After stimulation, we stained the surface proteins with a 40-plex BD® AbSeq Panel, including the Immune Discovery Panel and human Single-Cell Multiplexing Kit (SMK). Resting and stimulated cells were pooled, and a portion of the sample pool immediately underwent cell capture as a live control using a BD Rhapsody™ single-cell microwell system. The other portion of the pool was stored in BD® OMICS-Guard Sample Preservation Buffer for either 5 minutes or 24 hours followed by permeabilization, IC staining with a 10-plex IC AbSeq Panel (50-plex total, surface and IC), and cells captured on the the same system. We show IC protein detection for the expected positive targets, including pH2AX, actCaspase-3, and cPARP in the stimulated population, as well as T-Bet, Granzyme B and Helios in the resting lymphocytes. Analysis shows excellent correspondence between IC CITE-Seq protein and transcriptome for T-Bet, Granzyme B and Helios. The gene expression correlation for surface antigen and mRNA between the IC and live control each had R2 values > 0.9. The mRNA sensitivity for the IC sample compared to live control was within whole transcriptome analysis (WTA) assay variation at 86–89% and 95–97% median molecules and genes per cell. Our data showed that the new IC CITE-Seq assay can provide rich single-cell multiomics information including mRNA and surface and intracellular proteins. For Research Use Only. Not for use in diagnostic or therapeutic procedures.BD, the BD Logo and BD Rhapsody are trademarks of Becton, Dickinson and Company or itsaffiliates. © 2023 BD. All rights reserved. NPM-2532 (v1.0) 1023 Citation Format: Tracy L. Campbell, Adam Wright, Ying Wah Lee, Anne Tran, Yan Chen, Joe Olives, Hye-Won Song, Larry Wang. High-plexy intracellular and surface CITE-Seq offers multiomic insights into cell function [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB291.

Chitosan-LeoA-DNA Nanoparticles Promoted the Efficacy of Novel LeoA-DNA Vaccination on Mice Against Helicobacter pylori.

More than half of the world's population is infected with Helicobacter pylori (H. pylori), which may lead to chronic gastritis, peptic ulcers, and stomach cancer. LeoA, a conserved antigen of H. pylori, aids in preventing this infection by triggering specific CD3+ T-cell responses. In this study, recombinant plasmids containing the LeoA gene of H. pylori are created and conjugated with chitosan nanoparticle (CSNP) to immunize BALB/c mice against the H. pylori infection. We used the online Vaxign tool to analyze the genomes of five distinct strains of H. pylori, and we chose the outer membrane as a prospective vaccine candidate. Afterward, the proteins' immunogenicity was evaluated. The DNA vaccine was constructed and then encapsulated in CSNPs. The effectiveness of the vaccine's immunoprotective effects was evaluated in BALB/c mice. Purified activated splenic CD3+ T cells are used to test the anticancer effects in vitro. Nanovaccines had apparent spherical forms, were small (mean size, 150-250 nm), and positively charged (41.3 ± 3.11mV). A consistently delayed release pattern and an entrapment efficiency (73.35 ± 3.48%) could be established. Compared to the non-encapsulated DNA vaccine, vaccinated BALB/c mice produced higher amounts of LeoA-specific IgG in plasma and TNF-α in splenocyte lysate. Moreover, BALB/c mice inoculated with nanovaccine demonstrated considerable immunity (87.5%) against the H. pylori challenge and reduced stomach injury and bacterial burdens in the stomach. The immunological state in individuals with GC with chronic infection with H. pylori is mimicked by the H. pylori DNA nanovaccines by inducing a shift from Th1 to Th2 in the response. In vitro human GC cell development is inhibited by activated CD3+ T lymphocytes. According to our findings, the H. pylori vaccine-activated CD3+ has potential immunotherapeutic benefits.

Depression assessment using integrated multi-featured EEG bands deep neural network models: Leveraging ensemble learning techniques

Mental Status Assessment (MSA) holds significant importance in psychiatry. In recent years, several studies have leveraged Electroencephalogram (EEG) technology to gauge an individual's mental state or level of depression. This study introduces a novel multi-tier ensemble learning approach to integrate multiple EEG bands for conducting mental state or depression assessments. Initially, the EEG signal is divided into eight sub-bands, and then a Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN) model is trained for each band. Subsequently, the integration of multi-band EEG frequency models and the evaluation of mental state or depression level are facilitated through a two-tier ensemble learning approach based on Multiple Linear Regression (MLR). The authors conducted numerous experiments to validate the performance of the proposed method under different evaluation metrics. For clarity and conciseness, the research employs the simplest commercialized one-channel EEG sensor, positioned at FP1, to collect data from 57 subjects (49 depressed and 18 healthy subjects). The obtained results, including an accuracy of 0.897, F1-score of 0.921, precision of 0.935, negative predictive value of 0.829, recall of 0.908, specificity of 0.875, and AUC of 0.8917, provide evidence of the superior performance of the proposed method compared to other ensemble learning techniques. This method not only proves effective but also holds the potential to significantly enhance the accuracy of depression assessment.

On Migration As A Significant Challenge For The European Union

European society is currently facing several important challenges. One of the most significant is the challenge of mass illegal migration, which encompasses security, political, economic, social, societal, legislative and many other aspects. Migration thus represents a very complex problem, the solution of which is by no means not simple. It requires the adoption of a number of comprehensive, mutually synergistic measures and steps, both at the level of the European Union as a whole and at the level of individual Member States. That is also why the author deals with the issue of mass illegal migration to the EU in the article. The author´s interdisciplinary research primarily draws on official information and data published by competent European and other institutions, while using relevant scientific methods aimed at showing that migration represents a very complex and multifaceted phenomenon and, in recent years, one of the greatest challenges for the European Union.

Peculiarities of demand for medicines and assessment of the consequences of introducing co-payment in Kazakhstan

Object: In the context of the population's growing need for medical care and limited resources of the healthcare system, the issue of assessing the population's demand for medical services, especially for medicines, in both developed and developing countries becomes relevant. The study aims to determine the characteristics of the population's demand for medicines in the Republic of Kazakhstan and assess the impact of co-payment mechanisms on medicine consumption.
 Methods: To achieve this goal, we conducted a sociological survey of the population in all regions of Kazakhstan, with the participation of 1638 respondents. Questionnaire data were estimated using a logarithmic model.
 Findings: As a result of evaluating the logarithmic model, we identified the main factors influencing the demand for medicines. An individual's poor health state increases the demand for and consumption of medicines. We observed decreased demand for medicines among those individuals who assessed their health status by non-medical factors. Individuals who used the services of private medical institutions increased their demand for medicines, unlike those individuals who used the services of specialized medical institutions. A large amount of time spent traveling to medical centers is also one of the factors in increasing drug consumption. Such factors as visits to emergency medical care, the presence of chronic diseases in family members, the gender and place of residence of the individual, and a large number of children in the family influence high demand.
 Conclusions: The logarithmic model results show many factors influencing the demand for medicines; the introduction of co-payment further expands the variability of factors influencing the volume of medicine consumption by the population. Various co-payment mechanisms can both reduce consumption and encourage drug consumption.

Crime Registry Platform using Blockchain Ethereum and web3

Abstract: Criminal activities in India are on the rise, with many incidents going unreported. Despite the availability of an online portal for storing the First Information Reports (FIRs) handwritten FIRs always will remain common due to some of the traditional methods and practices. And probably in most cases, the complainants should personally visit the police stations in order to file a offense report. Crime and Criminal Tracking Network and Systems (CCTNS) was launched in 2010 for national wide e-governance, it will operates on a centralized system and it is particularly limited to some individual states. Therefore, there is a need for the decentralized solution in order to ensure failure of a single point and secure managing of criminal complaints from the unauthorized access.