Transactions on Engineering and Computing Sciences

Blockchain and Wireless Network Security: A Comprehensive Review of Techniques, Challenges, and Future Prospects

2025-05-31T03:18:19+01:00

In the history of digital ecosystems, security has always been and will continue to be a primary issue. Newer fields of study, such as blockchain and wireless networks, continue to confront the difficulties that are exclusive to them despite technological breakthroughs. This article provides an assessment of these two important domains, diving into their relevance and fundamental security methodologies, primary research, unsolved challenges, and prospective future paths. This article covers the key components of blockchain security and emphasizes the importance of wireless network security. A comparative study of the security of blockchain technology and wireless networks elucidates the distinctive qualities, difficulties, and solutions that each one presents. This paper also explores the synergistic integration of AI and blockchain to address the multifaceted security challenges in modern wireless networks.

Problems and Prospects of Risk Insurance in Tourism Activities in Transition Economies (Based on the experience of Uzbekistan)

2025-04-17T18:07:07+01:00

In the context of globalization and interstate integration, the concept of advanced tourism development is becoming one of the key areas for ensuring the sustainable economic growth of a country. An important aspect of this concept is the introduction of effective and accessible insurance protection mechanisms for both tourists and tourism organizations. The growth of global and domestic tourism is accompanied by an increased level of economic, socio-political, and environmental risks, which can significantly affect tourism activity and participants in the tourism market. The issues and prospects of risk insurance in the tourism sector remain insufficiently studied in both domestic and global economic science. This study aims to identify key problems and development prospects for insurance in the tourism sector of the Republic of Uzbekistan, which will help to develop effective strategies for minimizing risks and supporting the sustainable development of the country's tourism sector.

Recycled Leather and Sawdust Composite Sheets

2025-04-25T16:49:39+01:00

This study explores the innovative combination of leather waste and sawdust with polyurethane (PU) to fabricate composite sheets. The utilization of leather waste and sawdust addresses the environmental concerns associated with their disposal while harnessing their potential as sustainable resources. The composite sheets offer a promising alternative to traditional materials, showcasing desirable properties such as enhanced durability, flexibility, and eco-friendliness. The abstract highlights the significance of this research in advancing sustainable material development and promoting circular economy principles. The present work provides immense potential for a new approach to make composite sheets by recycling of leather waste and sawdust to safeguard the environment and maintain ecology. This composite sheet can be a good substitute as a sustainable alternative to synthetic leather which affects the environment adversely.

Quantum Computing and Applications: Its Effects on Cybersecurity

2025-05-10T18:25:27+01:00

Quantum computing is a developing field with the prospective to revolutionize the current advanced computing, it represents a fundamental shift from classical computing principles towards superposition, entanglement, and quantum interference, which are quantum mechanical principles. By using these distinctive principals and properties of quantum mechanics, ‘Quantum Computing’ processes information in the ways that are principally different from classical computers. This paper examines the fundamental concepts of quantum computing, its current and potential diverse applications, and the effects it has on cybersecurity. As quantum computing continues to advance, it proposes both forecasts and challenges, especially in cryptography, data protection, and security systems. This review paper focusses to discuss a comprehensive understanding of the junction between quantum computing and cybersecurity. It also point-outs the risks to current encryption theory and the new quantum-safe cryptography practices.

From the Pen to the Prompt: Scientific Authorship in the Shadow of AI. Who Thinks? Who Signs?

2025-05-07T19:33:26+01:00

This paper explores the redefinition of scientific authorship in the era of generativeartificial intelligence (GAI). Drawing on a reflexive approach, the study analyzes the impact of GAI on scientific creation processes while examining concerns about the dilution of intellectual responsibility. Through a literature review and an examination of current practices in academic settings, the author demonstrates that GAI does not eliminate human authorship but transforms it by offering new forms of collaboration between humans and machines. The main findings highlight the need to redefine scientific authorship by emphasizing intellectual intentionality and human responsibility, while integrating GAI as a tool for creative support.

Decoding Stock Trends: A Comparative Study of GRU, LSTM, and Transformer Models in Tech Sector Prediction

2025-05-14T22:40:54+01:00

This study investigates the predictive capabilities of three state-of-the-art neural network architectures—Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer models—in forecasting stock prices for five leading technology companies: Apple Inc. (AAPL), Cisco Systems, Inc. (CSCO), Meta Platforms, Inc. (META), Microsoft Corporation (MSFT), and Tesla, Inc. (TSLA). The dataset spans from July 2019 to July 2023, with models evaluated using key performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) values. The results show that GRU models consistently achieve the lowest MAE, indicating superior precision in stock price prediction. In contrast, LSTM models, while showing slightly higher error rates, are particularly effective in capturing long-term trends and the variance in stock prices, as evidenced by higher R² values. Transformer models, which utilize self-attention mechanisms, showed promise in handling complex relationships but struggled with the volatility of certain stocks, such as TSLA, leading to higher errors and lower R². These findings provide valuable insights for financial analysts and investment professionals, offering guidance on selecting the most suitable deep learning models based on specific market conditions and forecasting needs. This study also lays the groundwork for further exploration into model optimization and hybrid approaches for more robust financial forecasting.

The Future of Freight Pricing: AI Negotiation Agents Transforming RFPs and Spot Markets

2025-05-10T06:36:53+01:00

The freight industry demands rapid, equitable pricing solutions as 20% of loads move via volatile spot markets where shippers manually check rates through emails and bids and the rest rely on slow RFP processes. AI-powered negotiation agents revolutionize both by deploying real-time rate benchmarking, dynamic discount negotiation, and risk-adjusted pricing. For spot freight (1 in 5 loads), agents instantly align quotes with $150 billion in market data, keeping rates within 5% of the median ($50-$100/load savings), replacing hours of emails with seconds of precision. In RFPs, this shrinks timelines from months to days. Agents also negotiate spot discounts e.g., 7% off $1,000/load for 200 annual spot loads ($14,000 savings) and embed risks like 15% fuel swings into terms (e.g., 4% surcharge), ensuring stability across both channels. These innovations cut manual effort, boost transparency, and optimize profitability for shippers, brokers, and carriers. Despite hurdles like data access and adoption, AI negotiation agents herald a pricing future that unifies spot and contract logistics into an agile, data-driven ecosystem.

Industry 4.0, AI and Big Data: Technological Progress and Ethical Responsibilities

2025-05-15T00:54:53+01:00

Marking a new phase in digital transformation, Industry 4.0—also referred to as the Fourth Industrial Revolution—incorporates advanced technologies like IoT, AI, Big Data, robotics, cloud computing, and cybersecurity to revolutionize production systems, making them smarter, more efficient, and more flexible. The convergence of these technologies offers significant potential to improve efficiency, reduce operational costs, and enhance customization, while also introducing ethical challenges such as privacy breaches, cybersecurity risks, and algorithmic bias. This paper is structured into two parts: the first part explores Industry 4.0 and its associated technologies, including AI and Big Data; the second part examines the ethical implications of these technologies.

New Guidelines in Designing Buildings Through the Study of the Key Abilities of AI in Architectural Design Process

2025-05-15T00:17:02+01:00

The idea of artificial intelligence has changed as a result of technological developments and a better comprehension of the human mind. It now focuses on simulating human behavior rather than just carrying out intricate computations. As a result, there are numerous initiatives to incorporate AI technology into the architecture domain, interior designs encompassing design, administration, construction, real estate, and urban planning. The majority of AI solutions in this field are only somewhat intelligent at the moment, and crucial jobs still need a great deal of human supervision and involvement. There is Impact of Artificial Intelligence on Architectural Design and Construction wish provide more opportunities and challenges and could help in shaping a new design process through the integration of the artificial intelligent uses with the design process. This research highlights analytics for the ability of AI in Architecture by offering a new approach that could serve as new guidelines in designing building and inhabit our world

Detailed Analysis of AI Agents in Carrier Outreach for Freight Brokerage and Carrier Operations

2025-05-16T20:51:34+01:00

In the $1 trillion freight brokerage industry, securing the right truck at the right time and price depends on labour-intensive carrier outreach, where reps send daily bid emails to 1,000–3,000 carriers in a "spray and pray" approach, yielding only 10–15% booking rates after 4–6 hours of manual effort (Transport Topics, 2023). This paper investigates how AI agents, powered by Large Language Models (LLMs), can enhance this process by up to 80% in efficiency. These agents automate bid distribution by generating personalized emails using TMS data, parse carrier replies with 95% accuracy (NLP Benchmarks, 2024), respond to 80% of queries instantly, and negotiate rates in real time against $150 billion in DAT benchmarks (DAT, 2024), integrating seamlessly with load boards. Simulations of 20 loads across 2,000 carriers show response rates rising from 10% to 20%, bookings from 15% to 25%, and time dropping to 48–72 minutes daily, saving $50–$100/load ($1,000–$2,000 daily) based on $3.50/gallon fuel costs (EIA, 2024). In this automated world, brokers cover loads 25% faster, while carriers receive tailored bids, cutting outreach effort by 50%. Despite 6–12 month integration challenges (Gartner, 2024), AI agents transform carrier outreach into a proactive, data-driven ecosystem, optimizing the 20% of freight on spot markets (DAT, 2024) and beyond in a competitive landscape.