Next Generation Journal for The Young Researchers
New Generation Young Researchers Journal offers an innovative and inspiring publishing platform for young researchers. We aim to enable young scientists and researchers to share their work and make their voices heard in the academic world. Our journal presents the latest discoveries and developments in various fields of science and technology with a focus on interdisciplinary research.
Design of Functional Nanomaterials for Energy Storage Systems
The rapid global transition toward renewable energy and electrification demands advanced energy storage systems (ESS) that are efficient, durable, and environmentally sustainable. This research explores the design principles and synthesis strategies of functional nanomaterials for next-generation batteries and supercapacitors, emphasizing how nanoscale engineering can enhance energy density, charge transfer kinetics, and material stability. Conducted as a qualitative investigation, the study examines academic literature and industrial reports to identify trends in nanostructured materials—including metal oxides, carbon-based nanocomposites, and two-dimensional materials—that significantly improve electrochemical performance in modern energy devices. The analysis focuses on how surface morphology, porosity, and chemical composition influence key parameters such as ion diffusion, conductivity, and capacity retention. By mapping correlations between structure and function, the study elucidates the mechanisms through which nanomaterials optimize charge transport pathways and mechanical resilience under high cycling conditions. Additionally, it assesses the socio-economic and environmental implications of nanomaterial-based energy storage, considering factors like resource sustainability, recyclability, and scalability in industrial production. Through interdisciplinary synthesis, the research connects materials science, renewable energy engineering, and clean technology innovation, offering conceptual frameworks that illustrate how functional nanomaterials can contribute to low-carbon energy infrastructures. The findings highlight that intelligent nanomaterial design is central not only to enhancing energy storage efficiency but also to advancing the broader goal of sustainable technological progress.
functional nanomaterials, energy storage systems, batteries, supercapacitors, charge transfer, sustainability, nanostructure design, renewable energy
Copyright (c) 2025 Next Generation Journal for The Young Researchers
Integrated Quantum Photonic Architectures for High-Throughput Simulation and Optimization of Industrial Manufacturing Processes
The integration of quantum photonic architectures into industrial computation represents a transformative approach to addressing the growing complexity, precision demands, and energy inefficiencies of modern manufacturing systems. This research explores the development and application of integrated quantum photonic platforms for high-throughput simulation and optimization, enabling large-scale, parallelized modeling of manufacturing processes. Unlike traditional electronic-based computation, photonic architectures exploit the quantum properties of light—such as superposition, entanglement, and interference—to achieve massive computational concurrency with low latency and high energy efficiency. The study focuses on applying quantum photonics to enhance industrial process optimization, including workflow scheduling, supply chain coordination, defect detection, and material design optimization. Through simulations and conceptual modeling, the research demonstrates how photon-based qubits and integrated optical circuits can execute optimization algorithms more efficiently than classical architectures when managing large, interdependent datasets. The findings underscore the potential of quantum-enhanced photonic computing to achieve scalable, real-time decision-making in sectors such as semiconductor fabrication, automotive manufacturing, and energy systems engineering. Conducted in collaboration with academic scholars, the project provided hands-on exposure to algorithmic modeling, quantum heuristic design, and analytical evaluation, deepening the researcher’s understanding of both theoretical and applied quantum systems. Additionally, the experience cultivated transferable skills in critical thinking, interdisciplinary collaboration, and scientific communication—core competencies essential for the next generation of engineers and scientists navigating the quantum industrial era. This study ultimately highlights quantum photonics as a foundational enabler of intelligent, high-performance, and sustainable manufacturing ecosystems.
quantum photonics, integrated photonic circuits, industrial optimization, high-throughput simulation, quantum-enhanced manufacturing, scalability, quantum optics, computational efficiency
Design Automation of Quantum Circuits for Engineering Systems: Gate Optimization and Noise-Aware Compilation in NISQ-Aware Hardware
The rapid advancement of Noisy Intermediate-Scale Quantum (NISQ) devices has driven the need for efficient design automation tools that can translate complex engineering models into executable quantum circuits. This study explores the development of automated quantum circuit design frameworks focused on gate optimization and noise-aware compilation, enabling practical quantum computation for engineering applications such as optimization, simulation, and control systems. Traditional circuit design methods often fail to account for hardware constraints, decoherence, and gate infidelity, which significantly affect computational accuracy on NISQ devices. To address these challenges, this research introduces a hybrid automation approach combining quantum transpilation, variational circuit pruning, and error-aware synthesis algorithms to enhance circuit reliability and scalability. The framework integrates quantum-classical co-design principles to optimize both logical and physical circuit layers, leveraging hardware-specific calibration data to adapt compilation strategies dynamically. Through simulations and benchmark analysis, the proposed system demonstrates significant improvements in quantum resource utilization, error mitigation, and gate depth reduction compared to conventional compilation methods. This work underscores the growing importance of design automation in quantum engineering, where algorithmic efficiency must be harmonized with physical constraints to realize functional and scalable quantum workflows. By establishing a methodology for gate-level optimization and noise-adaptive design, the study contributes to bridging the gap between theoretical quantum algorithms and real-world engineering implementations within the NISQ computing era.
quantum circuit design, NISQ devices, gate optimization, noise-aware compilation, design automation, quantum transpilation, hybrid computation, quantum-classical co-design
Quantum Computing–Enhanced Service Ecosystems for Industrial Manufacturing Simulation and Workflow Optimization
The advent of quantum computing is redefining the computational landscape of industrial manufacturing, introducing new paradigms for simulation, optimization, and adaptive decision-making. This study investigates the integration of quantum computing–enhanced service ecosystems within industrial manufacturing frameworks, focusing on how quantum algorithms can accelerate workflow optimization, supply chain coordination, and process simulation in large-scale production environments. Traditional simulation and optimization techniques often struggle with the combinatorial complexity of modern manufacturing systems, leading to inefficiencies in resource allocation, scheduling, and predictive maintenance. The proposed framework leverages hybrid quantum–classical computation to simulate high-dimensional manufacturing workflows through quantum annealing, Quantum Approximate Optimization Algorithm (QAOA), and quantum-inspired reinforcement learning. These techniques enable more efficient exploration of multi-objective optimization spaces, facilitating near-real-time decision support and improved throughput under uncertain production conditions. The model also emphasizes the development of service-oriented architectures that integrate quantum computation with industrial Internet of Things (IIoT) infrastructures, digital twins, and cloud-based manufacturing services. Results from conceptual modeling and early simulation experiments suggest that quantum-enhanced service ecosystems outperform conventional digital twins and heuristic optimization methods in adaptability, precision, and energy efficiency. This convergence of quantum computing and industrial systems marks a shift toward next-generation intelligent manufacturing, capable of autonomous learning, dynamic reconfiguration, and sustainable production optimization.
quantum computing, service ecosystems, industrial manufacturing, workflow optimization, digital twins, quantum annealing, hybrid computation, smart industry, quantum optimization
The Role of Epigenetic Modifications in Evolution: Understanding How Environmental Factors Shape Heritable Gene Expression Across Generations
Epigenetic modifications provide a mechanism through which environmental factors can induce heritable changes in gene expression without altering the underlying DNA sequence. While the Modern Synthesis emphasises genetic mutation and natural selection as the primary drivers of evolution, growing evidence highlights the role of DNA methylation, histone modifications, and non-coding RNAs in enabling phenotypic plasticity and transgenerational inheritance. These mechanisms allow organisms to respond to environmental conditions more rapidly than genetic changes alone would permit, and in some cases, these responses are transmitted across generations via the germline. Empirical studies from both plant and animal systems demonstrate how exposures to stress, nutritional changes, and chemical disruptors can leave stable epigenetic marks. Additionally, large-scale genomic rearrangements such as chromoanagenesis point to the potential for abrupt structural variation to interact with epigenetic processes. Incorporating such findings within the framework of the Extended Evolutionary Synthesis expands traditional evolutionary models, offering a more integrated understanding of how environmental stimuli shape heritable variation and influence evolutionary trajectories over both short and long timescales.
epigenetics, gene expression, DNA methylation, histone modification, non-coding RNA, phenotypic plasticity, transgenerational inheritance, environmental stress, chromoanagenesis, extended evolutionary synthesis
The Impact of Workplace Diversity and Inclusion on Work-Life Balance
This study examines the association between diversity and work-life balance. Although it is known that workplace diversity can cause conflict and is referred to as an unfavorable issue, many studies have shown that diversity enhances work performance and work-life balance. In this article, I analyze multiple aspects of diversity considering its reflections in workplaces. The fact that people have different social characteristics indicates that companies need to update their inclusion policies. As companies embrace and internalize diversity, a positive impact is created in the career development and overall lives of employees in the workplace.
diversity, work-life balance, workplace diversity, inclusion policies, employee well-being, career development, workplace conflict, organizational culture, workforce inclusion, employee performance, workplace equity
Copyright (c) 2024 Next Generation Journal for The Young Researchers
The Impact of Diversity and Inclusion on Organizational Culture
Diversity and inclusion (D&I) have become central to modern organizational culture, shaping workplace dynamics, innovation, and overall business performance. A diverse workforce—encompassing differences in race, gender, age, background, and thought—fosters creativity, improves problem-solving, and enhances employee engagement. However, diversity alone is insufficient without an inclusive culture that ensures equitable opportunities, psychological safety, and a sense of belonging for all employees. Organizations that actively integrate D&I initiatives benefit from improved collaboration, greater adaptability in global markets, and stronger brand reputation. Yet, challenges such as unconscious bias, resistance to change, and performative inclusivity can hinder progress. This paper explores how D&I influences organizational culture, examining both the benefits and challenges through the lenses of leadership, policy implementation, and social psychology. By synthesizing research from management studies, behavioral science, and case studies of successful (and unsuccessful) corporate D&I efforts, this study provides a nuanced understanding of how organizations can build truly inclusive workplaces that enhance employee well-being and long-term business sustainability.
Diversity and Inclusion, Organizational Culture, Workplace Equity, Leadership, Employee Engagement, Innovation, Psychological Safety, Unconscious Bias, Corporate Social Responsibility
The Role of Cognitive Biases in Financial Decision-Making
Financial decision-making is often assumed to be a rational process guided by logical assessments of risk and reward. However, research in behavioral finance has consistently demonstrated that cognitive biases—systematic deviations from rational judgment—play a crucial role in shaping investment behavior, corporate decision-making, and personal financial choices. Biases such as overconfidence, loss aversion, anchoring, and herd mentality influence how individuals and organizations perceive financial information and respond to uncertainty. These biases can lead to suboptimal decisions, speculative bubbles, and market inefficiencies. Understanding the psychological underpinnings of financial behavior is essential for investors, policymakers, and financial professionals aiming to mitigate risks associated with irrational decision-making. This paper explores the most prominent cognitive biases affecting financial decisions, their implications for market behavior, and strategies for minimizing their adverse effects. By integrating insights from behavioral economics, psychology, and finance, this study highlights the importance of recognizing and addressing these biases to foster more informed and stable financial markets.
Cognitive Biases, Behavioral Finance, Decision-Making, Risk Perception, Overconfidence, Loss Aversion, Anchoring, Herd Mentality, Market Inefficiencies
Production of Algae-Based Carbon Black Pigment and Its Use in Carbon Neutral Printing Technologies
Within the scope of the project, we focused on the innovation potential in printing technologies, which is a common problem for all sectors, taking into account the contribution of chemistry to sustainability in innovation-oriented sectors. Based on the importance of microalgae in biomass, we obtained biochar for the production of carbon black pigment. Biochar provided an important result especially in the production of a pigment that has the potential to replace petroleum-derived carbon black pigment from fresh and salty algae such as Spirulina and Nannochloropsis. By using the raw pigment material we obtained together with environmentally friendly vegetable resins, we turned it into a product with low volatile organic compounds and no heavy metals. In this context, we examined the potential of the product formed in many dierent sub-elds of biochar printing technologies such as direct printing, UV curing and printing. With biocompatibility experiments, we also examined the status of the project in terms of human and environmental health. Thus, we have obtained preliminary findings on the transition from biomass to printing technologies, which has the potential for an important sectoral change. It will be possible to obtain more qualified and comprehensive products with future experiments. This transformation has the potential to affect many stakeholders for production and consumption.
Establishing a Relationship Between Inequality in Education and Inequality in Income
In this paper the relationship between inequality in education and inequality in the rate of income is explored. Many people from all round the world do not have access to quality education, which is a huge component of the Human Development Index (HDI). HDI is a summary measure of success in several crucial dimensions such as, long and healthy life, being knowledgeable, and the standard of living. Education, therefore, is a key component of the HDI as it is concerned with the standard of living as well as knowledge. Access to education is limited all around the world for a variety of reasons: gender inequality, income, lack of resources etc. Especially in the countries that are relatively poor (mostly in Sub-Saharan Africa) experience major obstacles in education inequality. In this sense, poverty status is correlated with the education gaps, especially when a country is facing severe problems in the distribution of income. In addition, residency in terms of rural or urban areas also does impact education inequality. Although many factors such as geographical location, education inequality, lack in research and development; the focus of the research is the correlation between education inequality and inequality in income distribution.
The Role of AI in Transforming Labor Markets: Productivity Gains Versus Job Displacement
Artificial Intelligence (AI) is reshaping labor markets globally, acting as a dual-edged force of transformation. While it promises unprecedented productivity gains and economic growth, it simultaneously raises concerns about job displacement and workforce polarization. This research examines the role of AI in transforming labor markets, analyzing its impact across industries and demographic groups. By evaluating case studies and leveraging econometric modeling, the study highlights the dichotomy between automation-driven efficiencies and the socio-economic challenges of technological displacement. Key findings reveal the potential for AI to enhance productivity, drive innovation, and create new job categories, while also exacerbating skill mismatches and income inequality. The study underscores the critical importance of proactive policies, including re-skilling initiatives, equitable AI adoption frameworks, and collaborative efforts between stakeholders, to mitigate adverse effects and ensure inclusive growth in an AI-driven economy.
The Economic Implications of Data Monetization in a Digital-First Economy
The digital-first economy has catalyzed an unprecedented shift in the valuation and utilization of data as a primary economic asset. This research delves into the multifaceted economic implications of data monetization, focusing on its role as a pivotal driver of growth, innovation, and policy development. As businesses increasingly leverage data to create new revenue streams, the study examines the evolving dynamics of value creation, competition, and inequality. It investigates the interplay between data-centric business models, market structures, and consumer privacy concerns, offering insights into how regulatory frameworks must adapt to balance innovation with ethical considerations. By analyzing case studies and employing econometric modeling, this research underscores the transformative potential of data monetization while addressing its societal and economic risks. Key findings highlight the necessity for equitable data-sharing mechanisms and global regulatory cooperation to prevent monopolistic tendencies and ensure inclusive economic benefits.
Cryptographic Algorithms in Decentralized Finance
Cryptographic algorithms form the backbone of decentralized finance (DeFi), ensuring secure, transparent, and tamper-proof transactions in blockchain ecosystems. These algorithms enable critical functionalities such as digital signatures, consensus mechanisms, and privacy preservation, empowering decentralized platforms to operate without intermediaries. This paper explores the role of cryptographic techniques, including asymmetric encryption, hash functions, and zero-knowledge proofs, in enabling DeFi applications like smart contracts, tokenization, and decentralized exchanges. Additionally, it examines emerging trends, such as post-quantum cryptography, to address vulnerabilities posed by quantum computing advancements. By investigating the intersection of cryptography and DeFi, this study highlights the challenges and opportunities in enhancing security, scalability, and interoperability within decentralized systems.
Machine Learning in Quantitative Finance
Machine learning (ML) has emerged as a transformative force in quantitative finance, offering novel tools and techniques for predictive modeling, risk management, and portfolio optimization. By leveraging large datasets and sophisticated algorithms, ML enables financial institutions to uncover complex patterns, generate predictive insights, and enhance decision-making processes. This paper explores the integration of machine learning methodologies into quantitative finance, focusing on applications such as algorithmic trading, credit risk assessment, and asset pricing. Furthermore, the study delves into challenges including overfitting, data sparsity, and model interpretability, proposing solutions to address these hurdles. The synergy between finance and machine learning underscores the potential for enhanced efficiency and innovation, paving the way for a paradigm shift in how financial systems operate.
Using Virtual Reality (VR) in Interior Design Visualization
The application of Virtual Reality (VR) in interior design visualization is redefining how spaces are conceptualized, experienced, and executed. This research investigates the integration of VR technologies in the interior design process, focusing on their ability to provide immersive, interactive, and realistic visualizations of spatial designs. By enabling clients and designers to explore virtual representations of proposed interiors, VR enhances communication, facilitates iterative design adjustments, and reduces the risk of costly post-construction changes. The study examines the technological underpinnings of VR, such as real-time rendering, 3D modeling, and haptic feedback, alongside their implications for design creativity, client satisfaction, and project efficiency. Furthermore, it addresses the potential of VR to democratize design by making sophisticated visualization tools accessible to a broader audience. Challenges such as the learning curve, technological costs, and integration into traditional workflows are also analyzed. This research contributes to the evolving discourse on digital transformation in design, emphasizing VR's role in bridging the gap between conceptualization and realization, and fostering a more dynamic and collaborative design environment.
The Impacts of Psychology and Neuroscience on Consumers’ Behaviour
The intersection of psychology and neuroscience has revolutionized our understanding of consumer behavior, offering profound insights into the cognitive and emotional processes that drive decision-making. This research explores the multifaceted impacts of psychological theories and neuroscientific advancements on consumer behavior, emphasizing how factors such as perception, memory, emotions, and neural mechanisms shape purchasing decisions. By integrating behavioral economics, cognitive neuroscience, and social psychology, the study highlights the role of implicit biases, heuristics, and neurochemical responses in influencing consumer preferences. Furthermore, the research examines the implications of neuromarketing techniques, such as brain imaging and biometric analysis, in predicting and shaping consumer behavior. Ethical considerations, including privacy concerns and the potential manipulation of consumer choices, are critically addressed to ensure responsible applications of these insights. By synthesizing theoretical models with empirical evidence, this study provides a comprehensive framework for leveraging psychology and neuroscience to enhance marketing strategies, improve consumer satisfaction, and foster ethical business practices.
Transgenerational Trauma and Psychological Resilience: A Multidimensional Approach
Transgenerational trauma, the transmission of psychological and emotional wounds across generations, remains a profound area of inquiry in understanding individual and collective well-being. This research investigates the multidimensional nature of transgenerational trauma, integrating insights from psychology, sociology, and neuroscience to unravel the mechanisms of trauma transmission and the development of resilience. By examining epigenetic processes, familial dynamics, and socio-historical contexts, the study highlights how traumatic experiences shape behaviors, beliefs, and coping strategies over time. It further explores resilience-building mechanisms, such as cultural practices, collective memory, and therapeutic interventions, that mitigate the effects of inherited trauma. Employing both qualitative and quantitative methodologies, including case studies and longitudinal surveys, this research presents a comprehensive framework for addressing transgenerational trauma and fostering intergenerational healing. The findings underscore the importance of interdisciplinary approaches in developing effective intervention strategies, emphasizing the role of community, storytelling, and systemic support in promoting psychological resilience. This work contributes to the growing discourse on trauma and resilience, offering actionable insights for clinicians, policymakers, and researchers dedicated to advancing mental health and societal healing.
Business Analytics Models in the Tech Corporates from Automative Sectors to Semiconductor Sectors
The integration of business analytics models in technology-driven corporations across automotive and semiconductor sectors is redefining decision-making, enhancing operational efficiency, and driving innovation. These models leverage advanced analytics techniques, including predictive modeling, machine learning, and optimization algorithms, to process complex datasets and generate actionable insights. In the automotive sector, analytics models facilitate advancements in supply chain optimization, predictive maintenance, and autonomous vehicle development. Similarly, in the semiconductor industry, analytics plays a pivotal role in yield optimization, defect detection, and demand forecasting. This research examines the diverse applications of business analytics across these sectors, highlighting how data-driven strategies address industry-specific challenges such as volatile demand, technological complexities, and competitive pressures. It also explores the interplay between analytics and emerging trends, such as Industry 4.0 and AI-driven manufacturing. By evaluating case studies and frameworks, the study provides insights into the strategic adoption of analytics to enhance decision-making and operational agility. The findings emphasize the transformative potential of business analytics in tech-driven industries, paving the way for smarter, more resilient corporate strategies.
AI-based Decision Making Process in the Finance Sector
The integration of Artificial Intelligence (AI) into the finance sector is revolutionizing decision-making processes, enabling data-driven insights and automation at an unprecedented scale. This research explores the transformative potential of AI in financial decision-making, focusing on its applications in risk assessment, portfolio management, fraud detection, and algorithmic trading. AI technologies, such as machine learning, natural language processing, and predictive analytics, empower financial institutions to process vast amounts of data efficiently, uncover patterns, and generate actionable insights. By enhancing precision and reducing human biases, AI-driven systems contribute to more informed and timely decisions. However, the adoption of AI in finance also raises concerns about transparency, ethical considerations, and regulatory compliance. This study aims to analyze the benefits and challenges of AI-driven decision-making, evaluate case studies of successful implementation, and propose frameworks for integrating AI ethically and effectively into financial operations. The findings emphasize the necessity of balancing innovation with accountability, ensuring that AI technologies enhance rather than compromise financial stability.
The Impact of Our Values System on Values Education
In an education system where passing exams takes precedence, we are raising a generation that loses itself in test books and remains unaware of values unless they encounter them in a reading comprehension question. Therefore, it is seen as a significant necessity to take measures that enable the education system to achieve its aims of cultivating good and effective individuals. Through this study, we sought to answer the question: Can we help students understand the importance of values education, encourage them to conduct research, and involve them in preparing various activities related to our values? Accordingly, we designed a task system aimed at increasing students’ awareness, helping them learn and internalize values, share them with each other, and develop a sense of responsibility and duty. This system, incorporating elements such as chance, fun, activities, encouragement for research, and creative writing, requires students to prepare diverse activities on topics related to values education. Additionally, students will learn about artists and statesmen who have contributed to our values at both national and international levels, as well as research the importance placed on these values in various cities and countries and the efforts made to instill them in society. The study aims to gain in-depth knowledge about the tasks undertaken, activities performed, and their contribution to values education through qualitative research. A detailed analysis was conducted, and the situation was evaluated in detail using observation and interview forms prepared in line with the research problems. The study was carried out with 15 eighth-grade students from a school in Antalya with similar social and economic conditions. Students’ names were coded as A1, A2, A3, etc. Through this system, students prepared numerous activities and conducted research on values, and their presentations were shared on bulletin boards. The involvement of students in the process demonstrated the system’s applicability.
National and International Contributions
Factors that Affect the Success of Pre-seed Level Startups
The success of pre-seed level startups is influenced by a complex interplay of factors spanning business strategy, team dynamics, market conditions, and access to resources. At this nascent stage, startups face unique challenges, including validating their business model, securing initial funding, and establishing a competitive edge. Key determinants include the founders’ expertise and vision, the scalability of the product or service, the clarity of the value proposition, and adaptability to changing market demands. The startup’s ability to leverage mentorship, build strong networks, and navigate the investor landscape also plays a critical role. This research examines these factors, emphasizing the importance of robust market research, efficient use of limited resources, and strategic planning. Furthermore, the psychological resilience of founders and the alignment of the team with the startup’s mission are explored as integral to overcoming early-stage obstacles. By synthesizing insights from case studies and empirical analyses, this study provides actionable recommendations for aspiring entrepreneurs to maximize their chances of success in the highly competitive pre-seed environment.
Role of Startups in Economic Growth: Evaluating How New Business Ventures Contribute to Job Creation, Innovation, and Regional Development
Entrepreneurship has become an engine of economic growth, playing a significant role in job creation, innovation and regional development. Startups contribute to business dynamism and adaptability by introducing disruptive technologies, challenging traditional businesses and stimulating competition. Their ability to quickly measure and respond to changing business needs allows them to solve important social problems while creating new jobs. This article explores the various roles of new businesses in driving economic growth by focusing on three key contributions: job creation, technological development and economic transformation in the region. It also highlights the unique characteristics of new businesses compared to traditional businesses, such as risk recovery and dependence on the business ecosystem. Through empirical research and data analysis, we explore how start-ups can foster innovation through partnerships, attract investment in underdeveloped areas, and support greater economic development. Despite their significant contributions, start-ups face many challenges, including high failure rates, limited access to capital, and regulatory challenges that can impact their development. By addressing these challenges through strategic initiatives and supporting business ecosystems, governments and stakeholders can increase the impact of new business opportunities on local, national, and international economies. The review highlights the importance of new businesses to ensure strong, resilient, and sustainable business in a rapidly changing world.
This study explains Turkey’s stance on policy rates throughout the 2010s and the reasons behind its recent monetary policy adjustments. Policy rate is a crucial tool for central banks which manages the economy, influences inflation, economic growth and employment. It is measured by basis points which is is the terminology to denote changes in interest rates. Turkey maintained an expansionary monetary policy throughout the 2010s to promote economic growth, and combat unemployment, maintaining relatively low interest rates. However, Turkey then shifted to a contractionary monetary policy in 2023 after the increasing inflation, currency devaluation and economic imbalances. This study also explains how the recent leadership changes in the Central Bank of Turkey have marked a new phase in the country’s monetary policy direction. In 2023, Hafize Gaye Erkan became the first female governor of the Central Bank, initiating a shift from an expansionary stance to an aggressive increase in policy rates. During her time in office, policy rates were raised significantly to address high inflation and currency instability, marking a clear change from earlier approaches. The behavior was then maintained by her successor, Fatih Karahan, by raising the policy rate by 50.0\% in March 2024. The paper highlights the significant effects of policy rate changes on Turkey's economy. It discusses their role in managing inflation, which hit record highs in 2023, and their impact on economic stability, investor trust, and financial markets. This paper also examines how external factors, like U.S. Federal Reserve decisions, affect Turkey’s monetary policy, especially regarding capital flows and exchange rates.
Defined as “the transdisciplinary study of how the arts and aesthetic experiences measurably change the body, brain, and behavior and how this knowledge is translated into specific practices that advance health and wellbeing” Neuroaesthetics is a field of science that is concerned with the question “How does art affect us physiologically and mentally, and how can this effect be used to improve wellbeing?” This paper examines the physiological processes behind perceiving beauty, and introduces different foundations that are using this phenomenon in order to improve mental health and well being. With the given information and the effectiveness of these foundations, this paper also provides my opinion on the convenience of Neuroaesthetics in mental health improvement, and emphasizes the importance of further focus and research on Neuroaesthetics as a form of therapy.
Applications of AI in Financial Fraud Detection
Artificial intelligence (AI) has emerged as a pivotal tool in combating financial fraud, offering advanced techniques to detect and mitigate fraudulent activities in real time. By leveraging machine learning algorithms, neural networks, and pattern recognition technologies, AI systems can identify anomalies, assess transaction risks, and predict potential threats with unparalleled accuracy. This research examines the various applications of AI in financial fraud detection, including its role in transactional analysis, user authentication, and predictive modeling. Key areas of focus include the integration of supervised and unsupervised learning methods, the use of natural language processing (NLP) for fraud analysis, and real-time data processing for dynamic threat management. The study also explores the challenges of implementing AI, such as data privacy concerns, algorithmic biases, and the need for regulatory compliance. By analyzing case studies and successful implementations, this research highlights best practices for leveraging AI in financial institutions to ensure robust fraud prevention. The findings underscore AI's transformative potential to enhance security, improve operational efficiency, and build trust in financial ecosystems.
Predictive Analytics for Sales Forecasting and Inventory Management
Predictive analytics has become a cornerstone of modern business operations, particularly in the domains of sales forecasting and inventory management. By leveraging historical data, machine learning algorithms, and statistical models, predictive analytics empowers businesses to anticipate future demand, optimize inventory levels, and enhance operational efficiency. This research explores the integration of predictive analytics into sales and inventory processes, focusing on its ability to reduce waste, minimize stockouts, and improve profitability. Key areas of investigation include the application of time series analysis, regression models, and AI-driven demand forecasting. The study also examines challenges such as data quality, integration complexities, and the need for real-time analysis in dynamic market conditions. Through case studies and industry insights, this research highlights best practices for implementing predictive analytics to achieve data-driven decision-making. Ultimately, it provides a roadmap for businesses to align predictive capabilities with strategic objectives, creating a more agile and competitive operational framework.
The Role of Blockchain in Securing Financial Transactions
Blockchain technology has emerged as a transformative solution for enhancing the security and transparency of financial transactions in the digital age. By leveraging its decentralized and immutable ledger, blockchain minimizes the risks associated with fraud, unauthorized access, and data breaches in financial systems. This research examines the role of blockchain in securing financial transactions, focusing on its core features such as distributed consensus, cryptographic security, and transparency. It explores real-world applications, including cryptocurrency transactions, cross-border payments, and smart contracts, highlighting their impact on reducing transaction costs and increasing trust. Furthermore, the study addresses challenges such as scalability, regulatory concerns, and energy consumption associated with blockchain technology. By evaluating existing use cases and future potentials, this research provides a comprehensive framework for integrating blockchain into financial systems. It underscores the importance of collaboration between financial institutions, regulatory bodies, and blockchain innovators to create a secure and sustainable digital financial ecosystem.
Data-Driven Strategies for Enhancing Customer Retention in E-commerce
In the highly competitive e-commerce landscape, customer retention is critical for sustaining business growth and profitability. This research explores data-driven strategies for enhancing customer retention by leveraging advanced analytics, machine learning models, and behavioral data. By understanding customer preferences, purchase patterns, and engagement behaviors, businesses can implement personalized marketing campaigns, loyalty programs, and product recommendations. The study examines how predictive analytics can identify at-risk customers and proactively address their needs through targeted interventions. Additionally, it highlights the role of real-time data processing and customer feedback analysis in optimizing retention strategies. Challenges such as data privacy concerns, algorithmic bias, and the ethical use of customer data are also addressed. This research aims to provide actionable insights for e-commerce businesses, enabling them to foster long-term customer relationships, reduce churn rates, and improve lifetime value (LTV) metrics. By combining technological innovation with customer-centric practices, the study offers a comprehensive framework for creating meaningful and sustained customer engagement in the digital age.
The Impacts of Anxiety on Highly Intellectual Students
Anxiety is a pervasive mental health challenge that can significantly affect the cognitive, emotional, and academic performance of students, particularly those identified as highly intellectual. This research explores the multifaceted impacts of anxiety on highly intellectual students, focusing on its influence on cognitive functioning, emotional well-being, and social interactions. By examining factors such as perfectionism, heightened self-expectations, and the pressure to excel, the study identifies key contributors to anxiety within this demographic. Utilizing qualitative and quantitative methodologies, including surveys, interviews, and cognitive assessments, this research aims to uncover patterns that differentiate the anxiety experiences of highly intellectual students from their peers. The study also investigates coping mechanisms, resilience factors, and the role of support systems in mitigating anxiety. By providing actionable insights, the research seeks to inform educators, mental health professionals, and policymakers on how to create supportive environments that foster well-being while maintaining academic excellence.
Hydrogel-based Soft Robotics for Surgical Machinery
Hydrogel-based soft robotics represent a transformative approach in surgical machinery, offering unparalleled adaptability, biocompatibility, and precision for minimally invasive procedures. Hydrogels, with their high water content and tunable mechanical properties, mimic soft biological tissues, making them ideal for applications in delicate surgical environments. This research explores the integration of hydrogel materials into soft robotic systems, focusing on their fabrication, actuation mechanisms, and performance in surgical applications. Key advancements include the development of stimuli-responsive hydrogels that enable precise control of movement and force, enhancing the capability to navigate complex anatomical structures. The study also examines computational models for simulating hydrogel behavior and optimizing robotic designs. While the potential benefits of hydrogel-based soft robotics in improving patient outcomes are significant, challenges remain in ensuring durability, scalability, and reliable control systems. This research aims to address these limitations by integrating advanced materials science with robotic engineering. By doing so, it contributes to the evolution of surgical technologies, paving the way for safer and more effective procedures.