Vol 2, No 1 (2023) |
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The Influence of Crosslinking Agents on the Properties of Thermoplastic Elastomers
Pages 1-12 || Authors: Jayadip GhanshyamBhai Tejani
ABSTRACT
This study investigates the effects of crosslinking agents on thermoplastic elastomers (TPEs) characteristics, emphasizing improving mechanical strength, thermal stability, and chemical resistance. Our main goal is to evaluate and contrast the impacts of various crosslinking techniques and substances on the performance of TPE. A systematic literature review methodology analyzes existing research articles, patents, and technical reports. Significant findings suggest that the mechanical properties of TPEs, such as tensile strength, elongation at break, and tear resistance, are greatly enhanced through crosslinking, which forms a strengthened polymer network. Crosslinked TPEs also exhibit improved thermal stability and resistance to chemical degradation, making them well-suited for a wide range of applications in the automotive, consumer goods, and medical device industries. Policy implications underscore the significance of choosing eco-friendly crosslinking agents to reduce environmental harm and encourage recycling. This study makes a valuable contribution to the field of material science, offering practical insights into optimizing TPE formulations for specific applications. It also emphasizes aligning with sustainability goals and regulatory standards, enhancing its significance.
Key words:
Crosslinking Agents, Thermoplastic Elastomers, Polymer Crosslinking, Material Properties, Mechanical Performance, Chemical Structure, Crosslinking Efficiency, Rheological Behavior, Thermal Stability
Cite as:
Tejani, J. G. (2023). The Influence of Crosslinking Agents on the Properties of Thermoplastic Elastomers. Silicon Valley Tech Review, 2(1), 1-12.
ABSTRACT
This study investigates the effects of crosslinking agents on thermoplastic elastomers (TPEs) characteristics, emphasizing improving mechanical strength, thermal stability, and chemical resistance. Our main goal is to evaluate and contrast the impacts of various crosslinking techniques and substances on the performance of TPE. A systematic literature review methodology analyzes existing research articles, patents, and technical reports. Significant findings suggest that the mechanical properties of TPEs, such as tensile strength, elongation at break, and tear resistance, are greatly enhanced through crosslinking, which forms a strengthened polymer network. Crosslinked TPEs also exhibit improved thermal stability and resistance to chemical degradation, making them well-suited for a wide range of applications in the automotive, consumer goods, and medical device industries. Policy implications underscore the significance of choosing eco-friendly crosslinking agents to reduce environmental harm and encourage recycling. This study makes a valuable contribution to the field of material science, offering practical insights into optimizing TPE formulations for specific applications. It also emphasizes aligning with sustainability goals and regulatory standards, enhancing its significance.
Key words:
Crosslinking Agents, Thermoplastic Elastomers, Polymer Crosslinking, Material Properties, Mechanical Performance, Chemical Structure, Crosslinking Efficiency, Rheological Behavior, Thermal Stability
Cite as:
Tejani, J. G. (2023). The Influence of Crosslinking Agents on the Properties of Thermoplastic Elastomers. Silicon Valley Tech Review, 2(1), 1-12.
Innovative AI Solutions for Defect Detection in Rubber Manufacturing Processes
Pages 13-26 || Authors: Vamsi Krishna Yarlagadda
ABSTRACT
This project aims to improve product quality, operational effectiveness, and cost-effectiveness by investigating novel artificial intelligence (AI) solutions for defect identification in rubber manufacturing processes. The key goals are to analyze implementation methodologies, explore prospects in AI-driven quality control, and evaluate AI techniques, including machine learning, computer vision, and sensor integration for automated defect identification. The methodology includes a thorough analysis of case studies, new developments in AI technology, and literature about defect identification in rubber manufacturing. Important discoveries demonstrate how AI-driven defect identification can reduce manual inspection work, increase accuracy, and reduce wasteful manufacturing. Policy consequences include issues with data quality, difficulties integrating technology, moral issues, and developing worker competencies. The present study highlights the revolutionary influence of artificial intelligence (AI) technologies on quality control procedures in the rubber manufacturing domain. It advocates for the prudent implementation and ongoing innovation to foster operational excellence and sustain industrial competitiveness.
Key words:
AI Solutions, Defect Detection, Rubber Manufacturing, Quality Control, Industrial Automation, Anomaly Detection, Process Optimization, Real-time Monitoring
Cite as:
Yarlagadda, V. K. (2023). Innovative AI Solutions for Defect Detection in Rubber Manufacturing Processes. Silicon Valley Tech Review, 2(1), 13-26.
ABSTRACT
This project aims to improve product quality, operational effectiveness, and cost-effectiveness by investigating novel artificial intelligence (AI) solutions for defect identification in rubber manufacturing processes. The key goals are to analyze implementation methodologies, explore prospects in AI-driven quality control, and evaluate AI techniques, including machine learning, computer vision, and sensor integration for automated defect identification. The methodology includes a thorough analysis of case studies, new developments in AI technology, and literature about defect identification in rubber manufacturing. Important discoveries demonstrate how AI-driven defect identification can reduce manual inspection work, increase accuracy, and reduce wasteful manufacturing. Policy consequences include issues with data quality, difficulties integrating technology, moral issues, and developing worker competencies. The present study highlights the revolutionary influence of artificial intelligence (AI) technologies on quality control procedures in the rubber manufacturing domain. It advocates for the prudent implementation and ongoing innovation to foster operational excellence and sustain industrial competitiveness.
Key words:
AI Solutions, Defect Detection, Rubber Manufacturing, Quality Control, Industrial Automation, Anomaly Detection, Process Optimization, Real-time Monitoring
Cite as:
Yarlagadda, V. K. (2023). Innovative AI Solutions for Defect Detection in Rubber Manufacturing Processes. Silicon Valley Tech Review, 2(1), 13-26.
From Silicon Valley to the World: U.S. AI Innovations in Global Sustainability
Pages 27-40 || Authors: Rajasekhar Reddy Talla; Srinivas Addimulam; Raghunath Kashyap Karanam; Vineel Mouli Natakam; Deekshith Narsina; Jaya Chandra Srikanth Gummadi; Arjun Kamisetty
ABSTRACT
This research investigates how Silicon Valley AI advances affect global sustainability. The goals are to analyze global adoption, sustainability, and ethical issues related to these technologies. The paper analyzes AI's worldwide spread, sustainability applications, and moral challenges using secondary data from literature, industry reports, and case studies. Significant results show that U.S. AI advances have improved environmental monitoring, resource management, and urban sustainability. Algorithmic prejudice, privacy problems, and AI's environmental impact threaten equal and sustainable results. The paper emphasizes the need for solid international legislation and ethical frameworks, AI infrastructure and education in underserved places, and energy-efficient AI technology. Policy implications emphasize the need for government, tech, and civil society collaboration to solve these concerns and assure AI's sustainable and egalitarian future.
Key words:
Artificial Intelligence, Silicon Valley Innovations, Global Sustainability, AI Diffusion, Environmental Monitoring, Digital Divide, Sustainable Development
Cite as:
Talla, R. R., Addimulam, S., Karanam, R. K., Natakam, V. M., Narsina, D., Gummadi, J. C. S., Kamisetty, A. (2023). From Silicon Valley to the World: U.S. AI Innovations in Global Sustainability. Silicon Valley Tech Review, 2(1), 27-40.
ABSTRACT
This research investigates how Silicon Valley AI advances affect global sustainability. The goals are to analyze global adoption, sustainability, and ethical issues related to these technologies. The paper analyzes AI's worldwide spread, sustainability applications, and moral challenges using secondary data from literature, industry reports, and case studies. Significant results show that U.S. AI advances have improved environmental monitoring, resource management, and urban sustainability. Algorithmic prejudice, privacy problems, and AI's environmental impact threaten equal and sustainable results. The paper emphasizes the need for solid international legislation and ethical frameworks, AI infrastructure and education in underserved places, and energy-efficient AI technology. Policy implications emphasize the need for government, tech, and civil society collaboration to solve these concerns and assure AI's sustainable and egalitarian future.
Key words:
Artificial Intelligence, Silicon Valley Innovations, Global Sustainability, AI Diffusion, Environmental Monitoring, Digital Divide, Sustainable Development
Cite as:
Talla, R. R., Addimulam, S., Karanam, R. K., Natakam, V. M., Narsina, D., Gummadi, J. C. S., Kamisetty, A. (2023). From Silicon Valley to the World: U.S. AI Innovations in Global Sustainability. Silicon Valley Tech Review, 2(1), 27-40.
AI-Driven Predictive Analytics for Risk Management in Financial Markets
Pages 41-53 || Authors: Narasimha Rao Boinapalli
ABSTRACT
This paper examines how AI-driven predictive analytics transforms financial market risk management by improving prediction accuracy, real-time monitoring, and decision-making. We identify essential AI deployment strategies and assess their advantages and drawbacks in financial institutions by reviewing secondary data, including academic literature and industry reports. The results show that AI analyzes complicated information and finds patterns that conventional approaches miss, improving risk evaluations. Real-time monitoring helps firms react quickly to emerging threats, improving operational resilience. The report also reveals algorithmic bias and model interpretability issues that might damage stakeholder confidence. Thus, policy implications imply regulators should promote openness and fairness in AI applications and encourage financial institutions to use explainable AI. This paper emphasizes the need for ethical AI to maximize predictive analytics advantages while addressing dangers by promoting cooperation among regulators, industry stakeholders, and technology developers. Financial institutions must use AI-driven methods to manage current markets and develop sustainably.
Key words:
Artificial Intelligence, Predictive Analytics, Financial Markets, Risk Management, Machine Learning, Data Analysis, Real-Time Monitoring
Cite as:
Boinapalli, N. R. (2023). AI-Driven Predictive Analytics for Risk Management in Financial Markets. Silicon Valley Tech Review, 2(1), 41-53.
ABSTRACT
This paper examines how AI-driven predictive analytics transforms financial market risk management by improving prediction accuracy, real-time monitoring, and decision-making. We identify essential AI deployment strategies and assess their advantages and drawbacks in financial institutions by reviewing secondary data, including academic literature and industry reports. The results show that AI analyzes complicated information and finds patterns that conventional approaches miss, improving risk evaluations. Real-time monitoring helps firms react quickly to emerging threats, improving operational resilience. The report also reveals algorithmic bias and model interpretability issues that might damage stakeholder confidence. Thus, policy implications imply regulators should promote openness and fairness in AI applications and encourage financial institutions to use explainable AI. This paper emphasizes the need for ethical AI to maximize predictive analytics advantages while addressing dangers by promoting cooperation among regulators, industry stakeholders, and technology developers. Financial institutions must use AI-driven methods to manage current markets and develop sustainably.
Key words:
Artificial Intelligence, Predictive Analytics, Financial Markets, Risk Management, Machine Learning, Data Analysis, Real-Time Monitoring
Cite as:
Boinapalli, N. R. (2023). AI-Driven Predictive Analytics for Risk Management in Financial Markets. Silicon Valley Tech Review, 2(1), 41-53.
Enhancing Cybersecurity in Distributed Systems: DevOps Approaches for Proactive Threat Detection
Pages 54-66 || Authors: Abhishekar Reddy Allam
ABSTRACT
This research examines how DevOps might proactively improve distributed system cybersecurity by detecting threats. The main goal is to discover and evaluate distributed environment security concerns and offer DevOps approaches that enhance attack detection and response. Research on cybersecurity vulnerabilities, threat detection tools, and DevOps techniques is synthesized via secondary data review. Critical studies show that distributed systems' complexity increases the attack surface, demanding a proactive security approach. The research emphasizes embedding security across the software development lifecycle, automating monitoring and incident response, and combining threat intelligence and behavioral analytics for real-time anomaly identification. Risk management requires a culture of security awareness and shared accountability among team members. Organizations must build comprehensive security frameworks that meet legal standards, encourage teamwork, and engage in continuing cybersecurity literacy training due to policy consequences. This study shows the importance of DevOps approaches in proactive threat detection and resilience, providing a path for firms seeking to improve cybersecurity in a complex threat environment.
Key words:
Cybersecurity, Distributed Systems, DevOps, Proactive Threat Detection, Threat Intelligence, Security Automation, Vulnerability Management
Cite as:
Allam, A. R. (2023). Enhancing Cybersecurity in Distributed Systems: DevOps Approaches for Proactive Threat Detection. Silicon Valley Tech Review, 2(1), 54-66.
ABSTRACT
This research examines how DevOps might proactively improve distributed system cybersecurity by detecting threats. The main goal is to discover and evaluate distributed environment security concerns and offer DevOps approaches that enhance attack detection and response. Research on cybersecurity vulnerabilities, threat detection tools, and DevOps techniques is synthesized via secondary data review. Critical studies show that distributed systems' complexity increases the attack surface, demanding a proactive security approach. The research emphasizes embedding security across the software development lifecycle, automating monitoring and incident response, and combining threat intelligence and behavioral analytics for real-time anomaly identification. Risk management requires a culture of security awareness and shared accountability among team members. Organizations must build comprehensive security frameworks that meet legal standards, encourage teamwork, and engage in continuing cybersecurity literacy training due to policy consequences. This study shows the importance of DevOps approaches in proactive threat detection and resilience, providing a path for firms seeking to improve cybersecurity in a complex threat environment.
Key words:
Cybersecurity, Distributed Systems, DevOps, Proactive Threat Detection, Threat Intelligence, Security Automation, Vulnerability Management
Cite as:
Allam, A. R. (2023). Enhancing Cybersecurity in Distributed Systems: DevOps Approaches for Proactive Threat Detection. Silicon Valley Tech Review, 2(1), 54-66.