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The Influence of Social Environment on Men and Women\u27s Sexuality
Get PDFThis article explores the influence of social environment on men and women\u27s sexuality. Psychological research and practice historically focused on gender dualism, but recent challenges to this assumption have led to complex and controversial terminology. Gender stereotypes are prejudices or inaccurate interpretations of various genders, and they exaggerate the differences between groups while underestimating the connections. In the 21st century, the application of social software has changed people\u27s love styles and sexual concepts. This paper conducts a literature review on the differences and influencing factors of male and female sexual concepts, factors affecting men and women\u27s sexual concepts in the 21st century, and new developments in research on men and women\u27s concepts
Applications of Cyber Threat Intelligence (CTI) in Financial Institutions and Challenges in Its Adoption
Get PDFThe critical nature of financial infrastructures makes them prime targets for cybercriminal activities, underscoring the need for robust security measures. This research delves into the role of Cyber Threat Intelligence (CTI) in bolstering the security framework of financial entities and identifies key challenges that could hinder its effective implementation. CTI brings a host of advantages to the financial sector, including real-time threat awareness, which enables institutions to proactively counteract cyber-attacks. It significantly aids in the efficiency of incident response teams by providing contextual data about attacks. Moreover, CTI is instrumental in strategic planning by providing insights into emerging threats and can assist institutions in maintaining compliance with regulatory frameworks such as GDPR and CCPA. Additional applications include enhancing fraud detection capabilities through data correlation, assessing and managing vendor risks, and allocating resources to confront the most pressing cyber threats. The adoption of CTI technologies is fraught with challenges. One major issue is data overload, as the vast quantity of information generated can overwhelm institutions and lead to alert fatigue. The issue of interoperability presents another significant challenge; disparate systems within the financial sector often use different data formats, complicating seamless CTI integration. Cost constraints may also inhibit the adoption of advanced CTI tools, particularly for smaller institutions. A lack of specialized skills necessary to interpret CTI data exacerbates the problem. The effectiveness of CTI is contingent on its accuracy, and false positives and negatives can have detrimental impacts. The rapidly evolving nature of cyber threats necessitates real-time updates, another hurdle for effective CTI implementation. Furthermore, the sharing of threat intelligence among entities, often competitors, is hampered by mistrust and regulatory complications. This research aims to provide a nuanced understanding of the applicability and limitations of CTI within the financial sector, urging institutions to approach its adoption with a thorough understanding of the associated challenges
Factors Affecting the Acceptance of Autonomous Vehicle Technology: A Multiple Regression Analysis
Get PDFThe acceptance of autonomous vehicle technology is a topic of growing interest and research. This study aims to identify the factors that influence the acceptance of autonomous vehicle technology using multiple regression analysis. The study collected data on safety concerns, cost, infrastructure, regulation, trust, human factors, technical limitations, and cultural factors from a sample of 500 individuals. The study found that all of these variables were significant predictors of the acceptance of autonomous vehicle technology in the multiple regression analysis. Specifically, safety concerns, cost, and trust were found to have the strongest impact on the acceptance of autonomous vehicle technology. These findings have important implications for policymakers, industry practitioners, and researchers interested in promoting the widespread acceptance of autonomous vehicle technology. By understanding the factors that influence acceptance, stakeholders can develop effective strategies to overcome barriers to acceptance and accelerate the transition to a future with autonomous vehicles. The findings of this study provide important insights into the complex interplay of factors that affect the acceptance of autonomous vehicle technology. The study contributes to the existing literature on this topic by using a comprehensive approach that considers a wide range of factors. The results suggest that promoting the safety and reliability of autonomous vehicles, addressing cost concerns, and building trust among the public are key priorities for stakeholders interested in accelerating the acceptance of this technology. Additionally, the study highlights the need for continued research and development to address technical limitations and overcome cultural barriers that may impede acceptance. Overall, this study underscores the importance of a multidisciplinary approach to understanding and promoting the acceptance of autonomous vehicle technology
From Autonomy to Accountability: Envisioning AI’s Legal Personhood
Get PDFThis paper critically examines the concept of granting legal personhood to artificial intelligence (AI) systems, addressing the challenges and implications within the context of evolving legal and societal frameworks. It navigates through the historical understanding of personhood, the ethical considerations posed by advanced AI capabilities, and the philosophical underpinnings of AI’s potential roles and responsibilities in society. By proposing a hypothetical scenario where AI is recognized with specific legal attributes, the study highlights the need for dynamic legal frameworks, international collaboration, and ethical AI development to ensure laws remain relevant and effective. The conclusion advocates for a multidisciplinary approach to crafting adaptable legal structures that acknowledge AI’s unique contributions to society while safeguarding human dignity and societal welfare, urging forward-looking policies that balance technological innovation with ethical and legal integrity
The Role of Artificial Intelligence In Accelerating International Trade: Evidence From Panel Data Analysis
Get PDFTechnology has historically played a role in shaping international trade, but the current explosion in Artificial Intelligence has the potential to radically alter global commerce in the years ahead. In this research, we hypothesized that the AI capability of a nation has a major impact on international trade. This study discusses different ways in which technological advancements in the AI domain are improving global trade. We tested the hypothesis using the WDI, Government AI Readiness Index panel dataset of 150 countries for the years 2018-2021. Fixed effect, and Random effect panel models were applied. The results show that the AI capability of a nation has a major positive influence on trade. The findings also show that GDP and exchange rate have significant positive impacts, and inflation and trade restrictions have negative and significant impacts on trade. The findings of this study recommend strengthening the nation’s AI capacity to increase its trade volume. AI will stimulate better economic development and open up new avenues for international trade to the extent that it fosters productivity growth. However, governments will need time to adapt and employ new AI technology, since doing so requires significant financial investments, access to skilled people, and a shift in how international companies are operated
Evaluating Arrow Dynamics via Stochastic Perturbation Methods
Get PDFTraditional methods of measuring arrow spine, involving static weight tests, fail to account for the dynamic behavior of arrows in flight. Addressing this gap, our study developed a novel apparatus to capture the dynamic properties of arrows, providing a more accurate reflection of their performance. By applying stochastic perturbations through a voice coil actuator and measuring displacements, we were able to determine the natural frequency, damping characteristics, and mechanical stiffness of arrows made from carbon, wood, and aluminum with varying spines. Our findings, based on a second-order parameterized model, correlated well with spine values provided by manufacturers. Additionally, extensive high cycle fatigue tests were conducted on each type of arrow material, revealing minimal impact on the dynamic parameters of the arrows
A Survey of Different IoMT Protocols for Healthcare Applications
Get PDFThe increasing use of wireless technologies in healthcare has provided new opportunities for remote patient monitoring, medical device communication, and electronic health record management. However, choosing the appropriate wireless technology for healthcare applications can be challenging due to their unique advantages and limitations. In this context, the following study explores the applications and limitations of various wireless technologies used in healthcare, including BLE, Zigbee, Wi-Fi, Cellular, LoRaWAN, NB-IoT, and Thread. BLE is commonly used for wireless data transfer from medical devices, remote patient monitoring, and location tracking. Zigbee is used for remote patient monitoring, medical device communication, and home health monitoring. Wi-Fi is used for remote patient monitoring, telemedicine, and electronic health record management. Cellular technology is used for remote patient monitoring, telemedicine, and emergency response. LoRaWAN is used for remote patient monitoring, asset tracking, and environmental monitoring. NB-IoT is used for remote patient monitoring and medical device communication. Thread is used for remote patient monitoring, asset tracking, and environmental monitoring. The study reveals that each wireless technology has its own unique advantages and limitations. For example, BLE has a limited range of up to 10 meters and limited bandwidth, while Zigbee has a range of up to 100 meters and limited bandwidth. Wi-Fi has high power consumption, which may not be suitable for battery-operated medical devices, while Cellular technology also has high power consumption and limited coverage in certain areas. LoRaWAN has limited bandwidth, and NB-IoT coverage may be limited in certain areas. Thread has a limited range and limited bandwidth. Our study recommend that healthcare providers should consider the range, bandwidth, power consumption, and reliability of communication to ensure that the chosen wireless technology meets the requirements of their application
Leveraging FAERS and Big Data Analytics with Machine Learning for Advanced Healthcare Solutions
Get PDFThis research study explores the potential of leveraging the FDA Adverse Event Reporting System (FAERS), combined with big data analytics and machine learning techniques, to enhance healthcare solutions. FAERS serves as a comprehensive database maintained by the U.S. Food and Drug Administration (FDA), encompassing reports of adverse events, medication errors, and product quality issues associated with diverse drugs and therapeutic interventions.By harnessing the power of big data analytics applied to the vast information within FAERS, healthcare professionals and researchers gain valuable insights into drug safety, discover potential adverse reactions, and uncover patterns that may not have been discernible through traditional methods. Particularly, machine learning plays a pivotal role in processing and analyzing this extensive dataset, enabling the extraction of meaningful patterns and prediction of adverse events.The findings of this study demonstrate various ways in which FAERS, big data analytics, and machine learning can be leveraged to provide advanced healthcare solutions. Machine learning algorithms trained on FAERS data can effectively identify early signals of adverse events associated with specific drugs or treatments, allowing for prompt detection and appropriate actions.Big data analytics applied to FAERS data facilitate pharmacovigilance and drug safety monitoring. Machine learning models automatically classify and analyze adverse event reports, efficiently flagging potential safety concerns and identifying emerging trends.The integration of FAERS data with big data analytics and machine learning enables signal detection and causality assessment. This approach aids in the identification of signals that suggest a causal relationship between drugs and adverse events, thereby enhancing the assessment of drug safety.By analyzing FAERS data in conjunction with patient-specific information, machine learning models can assist in identifying patient subgroups that are more susceptible to adverse events. This information is instrumental in personalizing treatment plans and optimizing medication choices, ultimately leading to improved patient outcomes.The combination of FAERS data with other biomedical information offers insights into potential new uses or indications for existing drugs. Machine learning algorithms analyze the integrated data, identifying patterns and making predictions about the efficacy and safety of repurposing existing drugs for new applications.The implementation of FAERS, big data analytics, and machine learning in advanced healthcare solutions necessitates meticulous consideration of data privacy, security, and ethical implications. Safeguarding patient privacy and ensuring responsible data use through anonymization techniques and appropriate data governance are paramount.The integration of FAERS, big data analytics, and machine learning holds immense potential in advancing healthcare solutions, enhancing patient safety, and optimizing medical interventions. The findings of this study demonstrate the multifaceted benefits that can be derived from leveraging these technologies, paving the way for a more efficient and effective healthcare ecosystem
Mining Public Opinion about Hybrid Working With RoBERTa
Get PDFAs the businesses recover from the COVID-19 epidemic, a new working paradigm is emerging: the hybrid work arrangement. A hybrid work method is a working approach that enables workers to work from several places, such as at home, on the move, or in the workplace. People are expressing their opinions on different social media outlets about the new work model. Organizations and businesses value public views. Because public perspectives will allow decision-makers to adapt promptly to rapidly transforming cultural, commercial, and social environments. Opinion mining is traditionally used to summarize the quantity of positive and negative responses in a given text using sentiment analysis techniques. Opinionated material from social media sites is used to identify people\u27s enthusiasm or displeasure with a certain issue under debate. This study analyzes the public sentiments (positive, negative, and neutral) on a hybrid work model using Twitter API and the Robustly Optimized BERT Pre-training Approach (RoBERTa). Out of 1 thousand tweets containing the term “hybrid work”, 37 (4.2%), 305 (33.3%), and 658 (62.5%) tweets were classified as negative, neutral, and positive, respectively. We also compared the public sentiments about hybrid work with those of remote work. The RoBERTa classified 8(1.6%), 436 (85.9 %), and 62 (12.5%) tweets as negative, neutral, and positive, respectively. The results showed that The majority of individuals showed favorable sentiment toward the hybrid work arrangement. The findings also demonstrate that “hybrid work” has an affinity with “remote work”, “ai”, “digital transformation” and “future of work”
Optimizing IT Modernization through Cloud Migration: Strategies for a Secure, Efficient and Cost-Effective Transition
Get PDFApplication migration to the cloud has become increasingly popular due to the numerous opportunities offered by cloud computing. It is a complex process that requires careful planning and execution to ensure a successful outcome. This research aims to provide an overview of the challenges and strategies involved in application migration to the cloud. The findings suggest that main challenges include compatibility, data migration, security and compliance, cost management, performance, and staffing and skills. To mitigate these challenges and ensure a smooth migration process, organizations must implement effective strategies. This research also discussed the effective strategies for cloud migration. These strategies include planning and preparation, assessment and prioritization, testing and validation, data management, security and compliance, cost management, and staffing and skills. The results of the research indicate that a well-defined migration plan, thorough testing, proper data management, strong security controls, cost optimization, and a skilled team are essential for a successful migration process. Organizations must carefully consider these challenges and strategies to ensure that their migration to the cloud is smooth, secure, and cost-effective. By addressing these challenges and implementing effective strategies, organizations can take advantage of the many benefits offered by cloud computing and modernize their IT infrastructure for the future