Search Results (8,840)

Distributed denial of service (DDoS) attacks pose a serious risk to the operational stability of a network for companies, often leading to service disruptions and financial damage and a loss of trust and credibility. The increasing sophistication and scale of these threats highlight the pressing need for advanced mitigation strategies. Despite the numerous existing studies on DDoS detection, many rely on large, redundant feature sets and lack validation for real-time applicability, leading to high computational complexity and limited generalization across diverse network conditions. This study addresses this gap by proposing a feature-optimized and computationally efficient ML framework for DDoS detection and mitigation using benchmark dataset. The proposed approach serves as a foundational step toward developing a low complexity model suitable for future real-time and hardware-based implementation. The dataset was systematically preprocessed to identify critical parameters, such as packet length Min, Total Backward Packets, Avg Fwd Segment Size, and others. Several ML algorithms, involving Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Cat-Boost, are applied to develop models for detecting and mitigating abnormal network traffic. The developed ML model demonstrates high performance, achieving 99.78% accuracy with Decision Tree and 99.85% with Random Forest, representing improvements of 1.53% and 0.74% compared to previous work, respectively. In addition, the Decision Tree algorithm achieved 99.85% accuracy for mitigation. with an inference time as low as 0.004 s, proving its suitability for identifying DDoS attacks in real time. Overall, this research presents an effective approach for DDoS detection, emphasizing the integration of ML models into existing security systems to enhance real-time threat mitigation. Full article

This research examines the relationship between corporate social responsibility (CSR) and perceived financial performance (FP) in the Saudi Arabian banking industry using the mediating variables of employee engagement (EE) and green creativity (GC). This study is based on the Social Identity Theory and considers CSR as an engine to produce ethical and social results and promote environmental innovation and sustainable competitiveness. According to a survey of 650 banking employees and structural equation modeling (SEM), the results show that CSR significantly and positively affects EE, GC, and FP, with EE having the strongest mediating role. These conclusions highlight the strategic consequence of CSR in advancing sustainability by balancing financial performance, employee welfare, and environmental innovation. This study adds value to the existing body of research because it provides information on the CSR-FP relationship in a developing economy, where such information is scarcely available. Consistent with the definition of sustainability, this study indicates how CSR activities combine social, environmental, and economic aspects to foster long-term organizational sustainability and sustainable development. Full article

With the development of uncertain finance, uncertain stock models have become increasingly popular for modeling stock prices. This paper explores the symmetric properties inherent in the uncertain mean-reverting stock model, particularly in the structure of its differential equations and the resulting pricing formulas. The primary findings comprise the derivation of explicit pricing formulas, via uncertain differential equations, for European, American, Asian, and geometric average Asian options under the uncertain mean-reverting stock model. The symmetry in the inverse uncertainty distributions and the duality between call and put options are highlighted, demonstrating the model’s alignment with symmetric financial principles. Furthermore, several numerical examples are provided to illustrate the applicability and the symmetry-related characteristics of the derived formulas. Full article

Real-time control of web traffic is a critical issue for network operators and service providers. It helps ensure robust service and avoid service interruptions, which has an important financial impact. However, due to the high speed and volume of actual internet traffic, standard multivariate time series models are inadequate for ensuring efficient real-time traffic management. In this paper we introduce a new model for functional time series analysis, developed by combining a local linear smoothing approach with an $L^1$-robust estimator of the quantile’s derivative. It constitutes an alternative, robust estimator for functional modal regression that is adequate to handle the stochastic volatility of high-frequency of web traffic data. The mathematical support of the new model is established under functional dependent case. The asymptotic analysis emphasizes the functional structure of the data, the functional feature of the model, and the stochastic characteristics of the underlying time-varying process. We evaluate the effectiveness of our proposed model using comprehensive simulations and real-data application. The computational results illustrate the superiority of the nonparametric functional model over the existing conventional methods in web traffic modeling. Full article

The digital economy (DE) has become an essential driver of sustainable growth under China’s “Dual Carbon” goals of carbon peaking and neutrality. However, limited evidence exists on the DE’s city-level effects on green and low-carbon transition. This study investigates the impact and mechanisms through which digital economy transition (DET) influences urban low-carbon development, utilizing panel data from 283 Chinese cities between 2011 and 2018. A comprehensive digital economy development (DED) index is constructed to measure regional digitalization levels. The findings reveal the following: (1) DET significantly improves CEE, and a one-standard-deviation increase in DED raises CEE by approximately 3.7%. (2) The effect of DET on CEE exhibits regional and resource-based heterogeneity, with western regions and resource-dependent cities benefiting more substantially. (3) The mechanisms through which DET improves CEE include stimulating the technological innovation level, attracting foreign direct investment (FDI), and promoting the financial development level. These insights provide valuable theoretical and practical implications for policymakers seeking to harness the digital economy to achieve sustainable urban development and carbon neutrality. Full article

Breaking barriers to sustainable jobs and promoting inclusive employment are key goals of the 2030 Agenda, with SDG8 Target 8.5 aiming to achieve decent work for all, including persons with disabilities (PWDs). This paper contributes to the scholarly debate by empirically examining how various regulatory areas, including credit market regulation, labor market regulation, business regulation, and the freedom to compete, influence the informal employment of PWDs in 15 countries between 2007 and 2022. The empirical investigation is conducted for the entire population with disabilities, as well as for adults and youth with disabilities. The analysis employs a dynamic labor demand function estimated through the two-step system GMM method to account for adjustment costs within the labor market. In addition, the Feasible Generalized Least Squares method is employed to assess the robustness of the results. The findings reveal significant heterogeneity in the effects of regulation on the informal employment of PWDs, with substantial differences between adults and youth. At the aggregate level, greater flexibility in most regulatory areas reduces informal employment of PWDs, except for labor market regulation. Upon examining age cohorts, the outcomes for adults exhibit similarities to the aggregate analysis. In contrast, more flexible regulations increase informal employment among young people with disabilities, except for business regulations, which exert negative impacts, and credit market regulations, which demonstrate no significant effects. This study recommends that policymakers support formal business development for PWDs and implement anti-discrimination laws. For youth with disabilities, targeted initiatives, including financial inclusion and wage subsidies, are essential to convert regulatory flexibility into formal employment opportunities. Full article

The study presents a scalable decision-support framework to assess energy-sharing strategies within mixed-use urban districts, with a focus on planning, sustainability, and policy relevance. Two renewable energy-sharing mechanisms—energy sharing (ES) and net metering (NM)—are compared through a techno-economic analysis applied to a real neighborhood in Montréal, Canada. The workflow integrates irradiance-aware PV simulation, archetype-based urban building modeling, and financial sensitivity analysis adaptable to local regulatory conditions. Key performance indicators (KPIs)—including Self-Consumption Ratio (SCR), Self-Sufficiency Ratio (SSR), and peak load reduction—are used to evaluate technical performance. Results show that ES outperforms NM, achieving higher SCR (77% vs. 66%) and SSR (40% vs. 35%), and seasonal analysis reveals that peak shaving reaches 30.3% during summer afternoons, while PV impact is limited to 15.6% in winter mornings and negligible during winter evenings. Although both mechanisms are currently unprofitable under existing Québec tariffs, scenario analysis reveals that a 50% CAPEX subsidy or a 0.12 CAD/kWh feed-in tariff could make the system viable. The novelty of this study lies in the development of a replicable, archetype-driven, and policy-oriented simulation framework that enables the evaluation of renewable energy communities in mixed-use and data-scarce urban environments, contributing new insights into the Canadian energy transition context. Full article

In recent years, the concept of artificial intelligence (AI) has garnered increasing scholarly and professional interest, particularly regarding its implementation across various domains, including journalism. As with any emerging technological paradigm, AI must be examined within its contextual framework to elucidate its potential advantages, challenges, and transformative implications. This study, situated within the theoretical lens of Actor–Network Theory, employs a mixed methods approach and, specifically, an explanatory sequential design to explore the integration of AI in contemporary Greek journalism. Primary data were collected through a structured questionnaire (N = 148) administered to professional journalists in Greece, followed by semi-structured interviews with a subset of participants (N = 7). The findings indicate that journalists perceive AI as a tool capable of enhancing work efficiency, minimizing human error, and facilitating the processing of unstructured data. However, respondents also expressed concerns that AI adoption is unlikely to lead to improved financial compensation and may contribute to job displacement within the sector. Additionally, participants emphasized the necessity of regular professional development initiatives, advocating for the organization of seminars on emerging technologies on a biannual or annual basis. Full article

Obesity has become increasingly prevalent, impacting up to 41 percent of women in the United States between 2021 and 2023, leading to a rise in short- and long-term adverse health events. With regard to reproductive health, obesity is associated with menstrual irregularities, poorer reproductive and obstetric outcomes, and an increased risk of endometrial cancer. Obesity can lead to hyperandrogenism and anovulation, which is consistent with polycystic ovarian syndrome (PCOS). The prevalence of obesity is higher in women with PCOS compared to the general population. Although PCOS increases the risk of obesity, not all women with PCOS are obese, and not all women with obesity develop PCOS. However, individuals with both PCOS and obesity often present with a more extreme phenotype, with increased risk of chronic anovulation, glucose intolerance, dyslipidemia, metabolic syndrome, vitamin D deficiency, and decreased fertility. Therefore, weight loss is the backbone of patient management in women with obesity and PCOS, and is associated with improvement in cardiovascular risk, as well as improvement in menstrual cycles, ovulation, and pregnancy rate. Lifestyle modifications are often the first-line intervention, with data supporting low glycemic index diets, including ketogenic and DASH diets, along with vitamin D supplementation to improve hormonal imbalances, insulin sensitivity, and menstrual cycles in those who do not have normal vitamin D levels. Furthermore, with the recent widespread adoption of newer FDA-approved medications for weight loss, including GLP-1 (glucagon-like peptide) receptor agonists, new data are emerging regarding the impact of PCOS and longer-term cardiovascular risk. The treatment of PCOS requires a personalized approach, with consideration of a patient’s reproductive goals, tolerance of risk, and acceptance of behavioral and financial commitments, as well as consideration of other medical comorbidities. This narrative review explores different weight loss treatment options, comparing lifestyle modifications (including diet, physical activity, mindfulness, stress management, and cognitive behavioral training), weight loss medications, and bariatric surgery and their respective impact on PCOS to assist clinicians in guiding their patients towards an effective, individualized intervention. Full article

In pursuit of sustainable development goal 5 (SDG5), this study underscores gender equity and women’s empowerment as pivotal themes in sustainable development. It examines the drivers of women’s empowerment, including education, economics, finance, and legal rights, using data from $n=223$ individuals, primarily women (68.4%) aged 20–30 (69.6%). The research methodology integrates descriptive statistical measures, machine learning (ML) algorithms, and graphical representations to systematically explore the fundamental research inquiries that align with SDG5, which focuses on achieving gender equity. The results indicate that higher educational levels, captured through ordinal encoding and correlation analyzes, are strongly linked to increased labor market participation and entrepreneurial activity. The random forest (RF) and support vector machine (SVM) classifiers achieved overall accuracies of 89% and 93% for the categorization of experience, respectively. Although 91% of women have bank accounts, only 47% reported financial independence due to gendered barriers. Logistic regression correctly identified financially independent women with a 93% recall, but the classification of non-independent participants was less robust, with a 44% recall. Access to legal services, modeled using a neural network, was a potent predictor of empowerment (F1-score 0.83 for full access cases), yet significant obstacles persist for those uncertain about or lacking legal access. These findings underscore that, while formal institutional access is relatively widespread among educated women literate in the digital world, perceived and practical barriers in the financial and legal realms continue to hinder empowerment. The results quantify these effects and highlight opportunities for tailored, data-driven policy interventions targeting persistent gaps. Full article

Understanding luxury tourists required a more comprehensive approach than traditional expenditure-based segmentation, which often overlooked travelers’ financial capacity. This study therefore aimed to develop and validate a new typology of luxury tourists by jointly analyzing income and expenditure patterns using the International Visitor Survey of South Korea. The study addressed the need to capture both tourists’ economic capability and consumption behavior to enhance the precision of market segmentation and support sustainable destination management. Using the Jenks natural breaks classification and logistic regression, four distinct tourist types were identified: economy, spurious, scrooge, and premier, each reflecting unique combinations of income and expenditure. The results revealed that age, nationality, occupation, and trip purpose significantly influenced tourists’ classification. Younger and middle-aged professionals from East Asia were more likely to belong to high-income and high-expenditure groups, whereas Western tourists tended to spend more relative to their income. This income–expenditure typology advanced theoretical understanding of luxury tourism segmentation and provided practical insights for destination marketing organizations. The findings offered new insights for understanding how the alignment between tourists’ financial capacity and spending behavior can redefine strategies for sustainable and inclusive tourism development. Full article

This paper assesses the correlates of perceived well-being among young women aged 18 to 30 in five Balkan cities: Athens, Greece; Plovdiv, Bulgaria; Bucharest, Romania; Nis, Serbia; and Shtip, North Macedonia, by integrating urban, travel behavioural, and socio-economic features. A cross-sectional survey was employed using standard questionnaires including the Warwick–Edinburgh Mental Well-being Scale (WEMWBS), the short version of the International Physical Activity Questionnaire (IPAQ), and the adapted ALPHA environmental questionnaire. To answer research questions, linear regression models were developed to analyse predictors of well-being at both regional and national levels. Results show that neighbourhood and mobility features play a significant role in shaping mental well-being. Access to walkable sidewalks, green spaces, mixed land-use structure, and attractive local facilities (e.g., shops, recreational centres in the neighbourhood) were consistently associated with higher levels of well-being. Conversely, perceived insecurity, especially at night or regarding bicycle theft, significantly reduced well-being. Physical activity levels, particularly days of walking and vigorous activity, showed strong positive associations, underscoring the role of active lifestyles in promoting mental health. Socio-economic variables, including financial status, relationship status, and work status, were also found to be linked to perceived well-being. Cycling-related variables may affect Greek well-being up to 16.5 times. Perception of crime during the night may negatively affect both Bulgarian and Serbian well-being (up to 10 times), while Romanian well-being is mostly affected by the existence of shopping facilities. Finally, the most impactful factors for well-being in North Macedonia refer to cycling safety and scooter accessibility. Full article

In this study, we review recent research on how digital payment technologies (DPTs) promote financial inclusion (FI) across the world. Drawing on empirical studies from the past decade, we show that digital payment systems have helped reduce financial exclusion—particularly in developing economies—by expanding access to essential financial services for underserved groups. The paper also highlights the role of demographic factors such as age and gender, with evidence of higher adoption among youth and women. We identify the main indicators used to measure digital payment adoption and FI, providing a foundation for future empirical analysis. To deepen understanding, we call for combining macroeconomic data with rigorous econometric approaches to better capture how DPTs contribute to inclusive financial systems. The paper further discusses how emerging innovations—including blockchain, artificial intelligence, cloud computing, and biometric authentication—are improving the efficiency, security, and accessibility of digital payments. Together, these technologies are likely to accelerate the transition toward fully digital financial ecosystems and expand the potential for inclusive and sustainable growth. Full article

Improving the well-being of rural residents remains a major policy challenge in developing countries. Previous studies have largely neglected the role of village leadership in influencing residents’ well-being. This study addresses this gap by examining the relationship between entrepreneurial village cadres (EVCs), defined as village leaders with entrepreneurial experience, and the subjective well-being (SWB) of rural residents in China. Using nationally representative data from the 2022 China Rural Revitalization Survey (CRRS), we found that EVCs significantly improve rural residents’ SWB. These results are robust to a series of identification strategies, including instrumental variable estimation and propensity score matching. Mechanism analysis reveals that EVCs exert their positive influence through three key channels: promoting income growth, enhancing democratic governance, and improving public services. Further heterogeneity analysis suggests that the effects of EVCs on SWB are more pronounced among non-poor households and in villages with external financial support. These findings enrich the literature on grassroots governance and well-being economics, while also offering practical implications for aligning leadership recruitment with broader goals of inclusive rural development. Full article

To evaluate the effectiveness of green finance, this study treats China’s green finance reform and innovation pilot zones as a quasi-natural experiment to assess their impact on urban energy efficiency. This research utilizes a panel dataset of 282 Chinese prefecture-level cities from 2010 to 2023 and employs a multi-period difference-in-differences (DID) model. The core dependent variable, urban green total factor energy efficiency (UGTFEE), is quantified using a non-radial Slack-Based Measure (SBM) efficiency model combined with the Malmquist-Luenberger index. The empirical findings reveal four key points. First, the green finance pilot zones significantly enhance UGTFEE, with policy-affected cities demonstrating an average improvement of approximately 2.0% relative to non-pilot cities. Second, this positive impact is transmitted through two primary mechanisms: the advancement of green technology research and development and the deepening of financial market development. Third, the policy’s effectiveness is heterogeneous, varying according to regional characteristics such as geographical location, environmental regulation stringency, and resource endowments. Finally, a negative spatial spillover is identified, wherein the policy creates a siphoning effect that competitively suppresses the UGTFEE of neighboring cities. These findings provide critical theoretical insights and empirical evidence for optimizing green finance initiatives, thereby facilitating urban industrial transformation toward greater green energy efficiency. Full article