World Problems Research Articles

Activismos digitales. Nuevas formas de lucha mediadas por plataformas en la era del capitalismo de la vigilancia

The academic concern for the role that activisms assume in the face of social problems in a troubled world maintains a pre-eminent relevance. Revolutionary waves are being registered as phenomena that express social demands. Understanding transmedia activism implies observing the reality beyond the struggles and polarizations of hashtags. In this context, what is understood by digital activism? How does the power dispute unfold in the digital territory? What is understood by performances? How is digital activism projected in the era of heightened Surveillance Capitalism? This paper aims to analyze academic productions that conceptualize, problematize and study digital activism. In addition, to characterize and understand their main features and identify trends and challenges that they present in the framework of Surveillance Capitalism. The methodological strategy was elaborated on the state of the art regarding digital activism. In order to analyze the current state of digital activism and its future evolution, search, selection, and bibliographic organization were included. Among the main findings, are the identification of studies that analyze the digital territory as a field of power dispute and the characterization of digital activism from social movements and collective political action and other forms of online participation, such as slacktivism.

МЕТОДИКАЛЫК ЧЫНДЫКТЫ КОЛДОНУУ АРКЫЛУУ МАТЕМАТИКАЛЫК БИЛИМДИ ГУМАНДАШТЫРУУ

The problem of humanization of mathematical education is an important pedagogical problem in our time, related to the upbringing of a comprehensively developed personality in the learning process. Study the global problems of the modern world–environmental, energy crises, etc. great hopes are pinned on a solution. In this regarded, it is important to know what the goals of science are, how it solves "human" problems, what role the concepts of morality, good and evil play in science, etc. It is important to know whether it becomes more humane. With the change in the social orientation of science, with the strengthening of its influence on the social world and man, its internal directions are also changing.

Enhancing Numerical Simulations through Advanced Data Analysis In Computational Mathematics

Improving numerical models through advanced data analysis in computational mathematics is a major step forward in many areas of science and engineering. The main goal of this study is to find ways to make accuracy, speed, and the ability to predict better by combining advanced data analysis techniques with computer simulation techniques. The study looks at how machine learning algorithms, statistical methods, and traditional computer methods can work together to solve hard, multidimensional problems. Algorithms for machine learning, like neural networks and support vector machines, are used to find trends and connections in very big datasets. These algorithms can give us information that other methods might miss. These new ideas make it easier to make models and programs that are more realistic. Statistical methods, like regression analysis and Bayesian reasoning, are used to measure error and make computer results more reliable. The goal of the study is to lower the cost of computing and raise the rate at which computer models converge by using these methods. A big part of this study is using these advanced methods to solve problems in the real world, like in material science, fluid dynamics, and climate models. Case studies show how combining computer simulations and data analysis can help make predictions that are more accurate and help people make better decisions. The study also looks at the problems that come with how hard these advanced methods are to compute and how much data they need, and it suggests ways to fix these problems. This study shows how mixing advanced data analysis with computer models in computational mathematics can change things. The results show how important it is to use methods from different fields to solve difficult science and engineering issues. This makes it possible for more accurate and useful models that can lead to new ideas and discoveries in many areas. This combination not only makes numerical models more powerful, but it also creates new study and use opportunities in computer mathematics.

Trends in the impact of suicide compared to other external causes of death: From 1995 to 2020

BackgroundSuicide is one of the world's greatest public health problems. More than 700,000 people lose their lives to suicide every year. While funding for mental health waits to be increased, thousands of suicides occur every day. Material and methodsThis study aims to quantify the global impact of suicide compared to other external causes of death in terms of years of potential life lost (YPLL), and how this will change between 1995 and 2020. Our source of information is the World Health Organization (WHO) mortality database. We then use YPLL, a standard measure of premature mortality and burden of disease that brings precision to the assessment of the impact of different causes of death. This, combined with the compound annual growth rate (CAGR) as a way of expressing increase, gives us a better understanding of the real situation and trends of suicide compared to other external causes of death in different countries worldwide. ResultsBased on the available sources of information and the selection criteria, we obtained a sample of 69 countries. The CAGR for all causes per capita decreased over the observed period in 65 countries, and it increases in 4 countries. In contrast, the CAGR specifically for suicide decreased in 49 countries, while an increase was observed in 20 countries. ConclusionsPrevention of most external causes of mortality shows promising data in most countries. However, this is not the case for suicide. Thus, YPLL due to suicide have decreased to a comparatively lesser extent and have even increased in some countries, a very worrying situation that poses many clinical and epidemiological challenges.

Implementing Project-Based Learning Using Artificial Intelligence

Project-based learning in the education system is gaining more and more momentum, as it is promising and contributes to the formation of all the necessary life skills for of students of professional preliminary higher education. It allows the development of their critical thinking and creative abilities. The application of artificial intelligence in education is an actual area of research that contributes to increasing the effectiveness of the educational process. Implementation of project-based learning using artificial intelligence (AI) is becoming increasingly urgent in education. This approach makes it possible to significantly increase the motivation of students, stimulate their independence and creativity, and prepare them to solve real problems in the modern world, where AI plays an increasingly important role. The purpose of the study is to highlight the possibilities and prospects of using artificial intelligence to implement project-based training of students of professional preliminary higher education. The research is conducted using mixed methods, including quantitative and qualitative approaches. Data are analyzed using statistical methods and interviews are conducted with participants in the educational process. During the research and presentation of the material, theoretical methods of scientific knowledge were used: analysis and synthesis, induction and deduction, systematization, and generalization. The author hypothesizes that the use of AI in project-based learning helps to increase the motivation of students, strengthens their independence, improves the skills of working with information and critical thinking, and also stimulates teamwork. It has been established that using artificial intelligence significantly increases the effectiveness of project-based learning. Its main advantages for teachers are described, including the automation of routine tasks. Positive results for students in the form of improved learning outcomes are summarized. Recommendations for the implementation of artificial intelligence services in the educational process, in particular, in project activities, are offered. The main areas for further research are systematized. The results of the study can be used for further development of the theory of project-based learning and artificial intelligence in education. The recommendations developed by the authors regarding the implementation of artificial intelligence in project-based learning. These recommendations can be used by teachers and lecturers to optimize the educational process, increase its effectiveness, and form the competencies necessary for a successful life in modern society for students. Further research can be aimed at developing new methods and tools for using AI in project-based learning, studying its impact on various aspects of students' educational activities, as well as studying the ethical aspects of using AI in education.

Credit Card Fraud Protection Application

Abstract – Credit card fraud detection is presently the most frequently occurring problem in the present world. This is due to the rise in both online transactions and e-commerce platforms. Credit card fraud generally happens when the card was stolen for any of the unauthorized purposes or even when the fraudster uses the credit card information for his use. In the present world, we are facing a lot of credit card problems. To detect the fraudulent activities the credit card fraud detection system was introduced. This project aims to focus mainly on machine learning algorithms. The algorithms used are random forest algorithm and the Adaboost algorithm. The results of the two algorithms are based on accuracy, precision, recall, and F1-score. The ROC curve is plotted based on the confusion matrix. The Random Forest and the Adaboost algorithms are compared and the algorithm that has the greatest accuracy, precision, recall, and F1-score is considered as the best algorithm that is used to detect the fraud.

Unveiling image forgery with cutting-edge techniques-a survey

Digital social platforms such as Facebook, Instagram, Snapchat, etc., driven by multimedia content, has led to billions of photos being shared annually. The ease of image modification has resulted in ‘deepfakes’, causing potential misinformation, emotional distress, and credibility loss. Image forgery has become a widespread problem with the advent of digital manipulation tools. Manipulated pictures are a problem in the digital world, demanding strong detection methods. This survey explores this important field and offers an extensive overview of state-of-the-art methods for identifying picture forgeries. Breaking down well-known techniques such as classic feature analysis, which unmask significant differences by examining pixels in depth and statistical irregularities. Moreover, this paper explores the revolutionary potential of deep learning by highlighting how convolutional neural networks (CNNs) can analyze pictures with unparalleled accuracy and reveal previously unseen alterations. By providing readers with a comprehensive grasp of the most recent developments in picture forgery detection with the datasets, this article gives them the knowledge they need to successfully negotiate the always changing field of visual deception. This survey acts as a launchpad for further research and development.

Social and health Problems of the contemporary World, Background and Perspectives

The contemporary universal healthcare industry is faced with a myriad of challenges that hinder public health efforts to a massive degree. These challenges include but are not limited to dental anxiety, physician burnout issues, and increasing stress among healthcare specialists. Nevertheless, concepts like patient-centric care, pharmacological outreach strategies, and digital practices in pharmacies are growing rapidly in Europe and worldwide, giving hope to the healthcare industry. Multidimensional approaches must be adapted and greatly implemented among patients, healthcare experts, and policymakers to ensure sustainable success in the healthcare industry.

Proteomic investigation of acute and chronic hypoxia/reoxygenation responsive proteins and pathways in H9C2 cardiomyoblasts.

Ischemic heart diseases continue to be a significant global cardiovascular problem in today's world. Myocardial reperfusion (R) is provided with an effective and rapid treatment; however, it can lead to fatal results, as well as ischemia (I). This study aims to use proteomic analysis to assess proteins and pathways in H9C2 cardiomyoblast cells exposed to hypoxic conditions, followed by reoxygenation, representing I/R injury for both short and long terms, reflecting acute and chronic hypoxia, respectively. Utilizing advanced techniques, our goal is to identify and characterize key proteins undergoing alterations during these critical phases. H9C2 cardiomyoblasts, a commonly used cell line for simulating in vivo I/R damage, were exposed to normoxia and hypoxia (0.4% O2) in six experimental groups: normoxia (3h), acute hypoxia (3h), acute hypoxia (3h) + reoxygenation (3h), normoxia (21h), chronic hypoxia (21h), and chronic hypoxia (21h) + reoxygenation (3h). Analyses were conducted using Nano LC/MSMS from tryptic digest of the whole cell lysates. Proteins were quantified using the label-free quantification (LFQ) algorithm in Proteome Discoverer 2.4. Proteomic analysis resulted in identification of 2383 protein groups. Proteins that differentially expressed in the various groups were identified (p < 0.05 among mean values for groups). Short-term hypoxia induces mitochondrial damage, energy demand, and cytoskeletal modifications. Chronic hypoxia triggers metabolic shifts, stress-response proteins, and extracellular matrix alterations. Data are available via ProteomeXchange with identifier PXD047994. Our research provides in-depth insights into how H9C2 cardiomyoblasts respond to both short-term and prolonged oxygen deprivation. Understanding hypoxia-related pathophysiology provides avenues for therapeutic intervention in hypoxia-related disorders.

Stochastic Models for Cyber Attack Detection and Response: A Mathematical Approach to Intrusion Detection Systems

Cyberattacks are a big problem in today's world because everything is linked online. They can damage the security, privacy, and access of private information. Intrusion Detection Systems (IDS) are very important for keeping networks safe because they quickly find and stop harmful activity. To successfully find and stop attacks, however, we need more advanced methods because online risks are always changing. This paper suggests a new mathematical approach for improving IDS that is based on random models. Traditional ways of finding intrusions often use signature- or anomaly-based methods, which might not be able to keep up with how attackers' strategies change all the time. By using random processes, our method provides a more flexible and adaptable way to find online threats. IDS can tell the difference between normal network traffic and hostile activities because stochastic models use a statistical framework to capture the uncertainty that comes with cyberattack behaviors. We use methods from probability theory, Markov chains, and queue theory to make models of how network traffic and possible cyberattacks might behave. Our random models can find differences that could mean someone is doing something bad by looking at the statistical features of different network factors, like the rate at which packets arrive, the length of a link, and the size of the payloads. Using Markov models, the IDS can also guess how likely it is that an attack will happen in the future based on past data, which helps with strategic strategies for reducing threats. Along with monitoring, our system includes ways to respond to cyberattacks and lessen their effects. By using stochastic optimization methods, we can change how resources are used and how reactions are prioritized based on how likely and how bad the threats are to be. This flexible method makes the network infrastructure more resistant to complex attack routes, which lowers the damage and downtime that can happen during cyber events. We show that our stochastic models can improve the performance of IDS in real-world situations by analyzing them theoretically and running simulations. Adopting a scientific approach to cyber security makes it possible for stronger and smarter defenses against cyber risks that are always changing.

A comparison among a fuzzy algorithm for image rescaling with other methods of digital image processing

The aim of this paper is to compare the fuzzy-type algorithm for image rescaling introduced by Jurio et al., 2011, quoted in the list of references, with some other existing algorithms such as the classical bicubic algorithm and the sampling Kantorovich (SK) one. Note that the SK algorithm is a recent tool for image rescaling and enhancement that has been revealed to be useful in several applications to real world problems, while the bicubic algorithm is widely known in the literature. A comparison among the abovementioned algorithms (all implemented in the MatLab programming language) was performed in terms of suitable similarity indices such as the Peak-Signal-to-Noise-Ratio (PSNR) and the likelihood index $S$.

Molecular survey of certain protozoan agents that cause diarrhea in children in Sudan.

Diarrhea is a significant health problem in the Third World. Identification of the pathogen that causes diarrhea is vital for measures to prevent and control this disease. There are also very few reports of diarrhea in Sudan. Our study aimed to determine the Prevalence of specific protozoan pathogens ( Entamoeba histolytica, Cryptosporidium parvum., and Giardia spp) in children in Khartoum, Sudan. We conducted a cross-sectional survey among children under five years of age who were hospitalized with acute diarrhea between April and December 2014. Diarrheal stool samples were collected and E. histolytica, C. parvum, and Giardia spp were examined using multiplex real-time PCR. Four hundred and thirty-seven children with acute diarrheawere included in this study; the higher Prevalence of diarrhea was in the age less than ≤ 2 years old (403,92.2%). The male-to-female ratio in this study was 1:1.7. infection with intestinal parasite was found in 155 (35.5%) cases, and co-infection was detected in 16 (10.3%) cases. Giardia spp(18.8%) and C. parvum (15.8 %) were the most frequently identified parasites, followed by E. histolytica (0.9). The parasite infection rate was highest and lowest in the under 2-year-old group (92.3%) and the 2-4-year-old group (7.3%). The infection rate was higher in boys (67.1%) than in girls (32.9%). The incidence of protozoan infection was higher in the rainy season (August to December) (92.2%), corresponding with that in the dry Season (April to June). (7.8%). Our present study demonstrated the high prevalence of Giardia spp and C. parvum in children with diarrhea in the Khartoum region and the usefulness of the multiplex real-time method in disclosing pathogenic protozoal agents. Our result highlighted the necessity of developing intervention measurement and control strategies to deal with childhood parasitic diarrhea in this region.

A multimodal multi-objective differential evolution with series-parallel combination and dynamic neighbor strategy

Multimodal multi-objective optimization problems (MMOPs), which aim to identify as many optimal solutions as possible and exhibit multiple equivalent Pareto optimal solution sets (PSs) that correspond to the same Pareto optimal front (PF), commonly arise in a wide range of optimization problems in the real world. However, some dominated solutions that exhibit greater diversity in the decision space may be substituted by non-dominated solutions with a higher level of decision space crowding. To tackle this issue, this paper proposes a multimodal multi-objective differential evolution with series-parallel combination and dynamic neighbor strategy (MMODE_SPDN), which can balance convergence, objective space diversity and decision space diversity. Specifically, two archives are initially updated serially followed by the overall update of the parallel structure, in which the serial-first approach can enhance population diversity and the parallel structure can greatly reduce the amount of calculation. In addition, a dynamic neighbor strategy which utilizes adaptive selection among neighbors to generate difference vectors in the decision space and objective space and then adopts the main and auxiliary parent method during the mutation process is proposed. Furthermore, the utilization of an auxiliary archive and the clustering-based special crowding distance (CSCD) method are employed to facilitate the updating of the archive, thereby enhancing diversity. MMODE_SPDN is compared with other multimodal multi-objective optimization evolutionary algorithms (MMOEAs) on numerous test problems and the experimental results demonstrate that MMODE_SPDN exhibits superior performance.

Correlation between diabetes mellitus and depression

Depression is the most prevalent mental health condition. It’s estimated that nearly 5% of adults suffer from this disorder globally. Depression is marked by ongoing sadness and a loss of interest or pleasure in activities that were once enjoyable or fulfilling. It can also lead to disruptions in sleep and eating patterns. Common symptoms include fatigue and difficulty concentrating. According to WHO diabetes is a chronic, metabolic disease characterized by elevated levels of blood glucose which leads to the damage of the heart, blood vessels, eyes, kidneys and nerves. Nowadays the occurrence of diabetes mellitus, obesity and depression is getting higher and higher. Both conditions stand as prominent health issues in today’s society. Recent pandemic, wars, problems of the current world can cause depression and anxiety disorders. The fast-paced nature of our lives, work-life balance issues, sedentary habits, and time constraints can lead to opting for fast food and neglecting a healthy diet. People with diabetes are more prone to experiencing depression. Moreover, a significant portion of cases remains underdiagnosed.

Introducing a Parallel Genetic Algorithm for Global Optimization Problems

The topic of efficiently finding the global minimum of multidimensional functions is widely applicable to numerous problems in the modern world. Many algorithms have been proposed to address these problems, among which genetic algorithms and their variants are particularly notable. Their popularity is due to their exceptional performance in solving optimization problems and their adaptability to various types of problems. However, genetic algorithms require significant computational resources and time, prompting the need for parallel techniques. Moving in this research direction, a new global optimization method is presented here that exploits the use of parallel computing techniques in genetic algorithms. This innovative method employs autonomous parallel computing units that periodically share the optimal solutions they discover. Increasing the number of computational threads, coupled with solution exchange techniques, can significantly reduce the number of calls to the objective function, thus saving computational power. Also, a stopping rule is proposed that takes advantage of the parallel computational environment. The proposed method was tested on a broad array of benchmark functions from the relevant literature and compared with other global optimization techniques regarding its efficiency.

The era of the “golden workers” and the collective bargaining challenges for the age management in companies

The ageing of the population is a fact, and also a worldwide social concern. There are two underlying problems in the business world that must be connected: On one hand, the need for older people to remain in the labour market and, on the other hand, the prejudices that exist in the business world in relation to older people. The plans known as generational diversity plans are essential for keeping older people in the labour market but, taking into account previous experiences, these plans should be developed in the field of the collective bargaining, preferably in a mandatory manner, which is not currently the case.

Iterative Construction of Conserved Quantities in Dissipative Nearly Integrable Systems.

Integrable systems offer rare examples of solvable many-body problems in the quantum world. Because of the fine-tuned structure, their realization in nature and experiment is never completely accurate, and therefore effects of integrability are observed only transiently. One way to overcome this limitation is to weakly couple nearly integrable systems to baths and driving: this will stabilize integrable effects up to arbitrary time and encode them in the stationary state approximated by a generalized Gibbs ensemble. However, the description of such driven dissipative nearly integrable models is challenging and no exact analytical methods have been proposed so far. Here, we develop an iterative scheme in which integrability breaking perturbations (baths) determine the conserved quantities that play the leading role in a highly efficient truncated generalized Gibbs ensemble description. Our scheme paves the way for easier calculations in thermodynamically large systems and can be used to construct unknown conserved quantities.

Complete inference via knowledge Petri nets and resolution rules

A novel inference approach is presented based on knowledge Petri nets and resolution rules. First, a knowledge base is modeled as a knowledge Petri net, where logical clauses are represented by monitor places, called semantic places. Second, a resolution pair is defined to identify a pair of semantic places, which, corresponding to two clauses that can be resolved, can be used to generate a new place in the knowledge Petri net. Such a new place represents an unknown clause implied by the knowledge base. Third, a method is proposed to decide whether a resolution inference is redundant based on the resolution pair. The size of a knowledge Petri net can be reduced, and the inference computation is saved in this way. Fourth, an inference algorithm is proposed employing the resolution pair and knowledge Petri net, proven to be sound and complete. Its computational complexity is polynomial with respect to the number of logical variables and clauses. In addition, another inference algorithm is developed for a given complete knowledge base and a set of newfound clauses. The algorithm is proven to be sound and complete, which offers significantly reduced computational complexity and specifically is linear concerning the number of new clauses. Finally, the wumpus world problem is taken as an example to illustrate and verify the proposed inference algorithms.

A reinforcement learning assisted evolutionary algorithm for constrained multi-task optimization

Multi-task optimization problems in the real world often contain constraints. When dealing with these problems, it is necessary to consider multiple tasks and their respective constraints simultaneously. However, most of existing research on multi-task optimization neglects the influence of constraints, which leads to slow convergence speed and susceptibility to local optima. To address the aforementioned issues, this paper proposes a reinforcement learning assisted constrained multi-task evolutionary algorithm. First, to meet the different requirements of different tasks and constraints, an adaptive operator selection strategy based on reinforcement learning is proposed. Second, to enhance population diversity, a multi-population method with different constraint handling techniques is introduced. This method assigns two independent populations to each task. The main population aims to find feasible solutions, while the auxiliary population focuses on exploring the entire search space. Finally, considering the individual differences between tasks, a dimension-based knowledge transfer is employed to facilitate positive information exchange. Compared with other state-of-the-art constrained evolutionary algorithms, the experimental results on constrained multi-task benchmark suite demonstrate the superiority of the proposed algorithm. The source code can be obtained from https://github.com/yufeiyng/RL-CMTEA.

LIFE SKILLS AWARENESS: A STUDY OF HIGHER SECONDARY STUDENTS

In today's rapidly evolving educational landscape, the primary focus has traditionally been on imparting knowledge and information. Student success was often measured by their ability to excel in examinations, achieve academic accolades, and pursue careers requiring specialized knowledge in a specific field. However, this paradigm is shifting. Education is no longer confined to textbooks and conventional classrooms. It is imperative for students to not only excel academically but also master the art of living.Life skills, often called soft skills or essential skills, are gaining prominence as an integral component of education. These abilities behaviors, and knowledge extend beyond academic disciplines and equipment for navigating the real- world problems. While academic knowledge remains undeniably important, life skills provide the foundation for leading successful, fulfilling lives. The realization that students need more than just scholastic achievement has led to a reinterpretation of educational goals. This research tries to examine the significance of life skills in education, their multifaceted nature, and their capacity to prepare students for success in a society that values practical knowledge, interpersonal skills, and adaptability as much as academic knowledge. By examining communication skills, problem-solving techniques, financial literacy, time management, emotional intelligence, and digital literacy, we will explore how these components of life skills are the cornerstone of overarching education.The study focused on Class XII students from two higher secondary schools in Aizawl District. A sample of 150 students (68 male, 82 female) were randomly selected from the population. A self-developed questionnaire was administered to the students during classroom visits. Responses were kept confidential. Statistical techniques such as mean, standard deviation, and t-tests were employed for data analysis and assess life skills awareness of each respondent.The analysis revealed that significant differences were found among male and female students in effective communication skills, accompanied by higher scores of male students. However, no significant differences were found between male and female students in critical thinking skills. Additionally, no noteworthy variations were observed among students from nuclear and joint families in any of the assessed life skills. While this study offers insightful information about life skills awareness among higher secondary students, more investigation or research is needed to explore the factors influencing the development of these skills in order to cultivate effective strategies for incorporating them into educational curricula. By emphasizing the importance of life skills, educational institutions can empower students to navigate the challenges of the 21st century and lead successful, fulfilling lives.