Volume 13 | Issue 7 |

 Abstract

In big cities, traffic congestion is a major problem that is frequently brought on by static traffic light systems that don't take into account the flow of vehicles in real time. Due to their reliance on pre-programmed timings, traditional traffic signals cause needless delays, fuel waste, and ineffective traffic management. This study suggests an intelligent and well-coordinated traffic light control system that uses real-time video surveillance and artificial intelligence (AI). Using video feeds at crossings, the system analyses and detects vehicle density using computer vision algorithms. This analysis is used to dynamically modify signal timings in order to give priority to lanes with greater congestion. Additionally, by connecting several traffic lights via a communication protocol, the system allows neighbouring junctions to synchronize their signal phases. Green corridors are created, stop-and-go traffic is decreased, and general urban mobility is enhanced by this networked strategy. A centralized communication layer facilitates inter-signal data exchange, signal timing adjustment techniques, and real-time vehicle recognition utilizing YOLO models. To test the system, real-world circumstances are replicated through simulation using Python and OpenCV. According to preliminary findings, when compared to fixed-timer systems, the suggested approach greatly lowers vehicle waiting times and enhances traffic flow efficiency. This study advances the creation of intelligent transportation systems and offers smart cities dealing with escalating traffic problems a scalable answer.

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 Keywords

Artificial Intelligence, Traffic Signal Control, Smart Cities, Computer Vision, Real-Time Systems, Adaptive Traffic Management, Interconnected Networks.

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Quantum cryptography is a development that is crucial in improving cybersecurity, because it is based on quantum mechanics concepts and can be used to produce genuine random numbers, which are important in a strong cryptography. The abstract summarizes the findings of 30 major researches, which discuss different quantum random number generators (QRNGs) and their use in securing digital communications. The emergent distinction between QRNGs and classical pseudorandom number generators is noted in many studies pointing out the novel properties of quantum mechanics to generate indeed random numbers [1][2]. It is important to note that research highlights the need to have device-independent and semi-device-independent QRNG protocol that would offer security guarantees against adversarial tampering without requiring the security of the used devices [3][4]. Additionally, information that is known about entropy sources, approaches to extracting randomness, and the rigorous statistical justification [5][6] of high-quality RNG confirms that such RNG can be even more important in cryptography and information security. The enhanced method of public verification and combination of QRNGs and cloud services have bright prospects in a number of services in the finance and data privacy industry [7][8]. To sum up, quantum technologies will become even more diversified on an ongoing basis, and their implementation into cybersecurity systems can serve as an even stronger protection against new threats, which is why it is impossible to overestimate the role of quantum technologies in the security of the future of digital communications [9][10].

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Quantum Random Number Generator, Cryptographic Security, Quantum Key Distribution, Randomness Bias, QRNG Limitations

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 Abstract

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 Keywords

Rice Tungro Disease (RTD); RTSV; RTBV; CP3 Capsid Protein; Protein Purification; Fluorescence Spectroscopy; Virus-like Particles (VLPs); Transmission Electron Microscopy; Plant Virology

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Rice Tungro disease poses a significant challenge to rice cultivation throughout Southeast Asia and arises from coinfection by Rice Tungro Bacilliform Virus (RTBV) and Rice Tungro Spherical Virus (RTSV). RTSV plays a critical role in enabling the efficient transmission of RTBV through its insect vector, the green leafhopper (Nephotettix virescens). Among the viral components, the CP3 capsid protein of RTSV is essential for assembling virions and mediating interactions with both the host and the insect vector. In this investigation, we employed a range of bioinformatics tools to carry out an in silico characterization of the CP3 gene. The gene's nucleotide sequence was first translated into its amino acid counterpart, followed by an in-depth analysis of its physicochemical properties, secondary and tertiary structural features, and immunogenic potential. Using AlphaFold predictions and comparative homology modeling, high-confidence three-dimensional structural models were constructed and validated through tools such as Ramachandran plot evaluation, ProSA-web scoring, and Verify3D analysis. The core ?-barrel architecture, which is typical of viral capsid proteins, exposes potential surface epitopes, which may be involved in immune recognition or interactions with the insect vector [12]. Furthermore, docking simulations and electrostatic surface potential analysis were used to pinpoint likely binding interfaces for host or vector proteins. Strong sequence conservation among varied geographical RTSV isolates was discovered by evolutionary analysis, highlighting the possibility of broad-based diagnostic markers and resistance targets. Overall, this study provides detailed structural and functional insights into the RTSV CP3 protein and supports its relevance for applications in diagnostics, rice tungro infection management or pesticide development, and engineered resistance. It also shows how computer modeling can considerably progress plant virology, particularly in the lack of experimental structural evidence.

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 Keywords

RTBV, RTSV, CP3, capsid, TEM, VLP

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This paper explores the development and implementation of AI-based workload forecasting techniques to address the complex, dynamic demands faced by e-commerce cloud platforms. With the rapid growth of online retail and the accompanying fluctuations in user activity, traditional static and statistical forecasting methods often fail to provide the accuracy and responsiveness required for optimal cloud resource management. This research investigates advanced machine learning models, including LSTM, GRU, and Transformer architectures, and benchmarks their performance against conventional time series approaches like ARIMA and Prophet. Using real-world e-commerce workload data, the study demonstrates that deep learning models significantly enhance forecast precision, especially during peak demand periods driven by promotional events and shifting consumer behavior. A pilot deployment further validates the models' practical impact on dynamic resource scaling, cost efficiency, and service reliability. By integrating explainable AI techniques, the paper also addresses the need for interpretability and stakeholder trust in automated forecasting systems. The findings highlight both the transformative potential and the operational challenges of adopting AI for workload prediction, offering actionable insights for e-commerce businesses and cloud providers aiming to build more intelligent, resilient, and sustainable digital infrastructures.

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AI-based workload forecasting, e-commerce cloud platforms

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Background: Neurological rehabilitation aims to enhance functional abilities and well-being in individuals with nervous system disorders.The Lumosity brain training program is one example of Computerized Cognitive Training (CCT), which has become a potential technique for improving cognitive function in a variety of neurological diseases. Objective: This review evaluates the efficacy of Lumosity-based CCT in improving cognitive function in patients with neurological disorders by analyzing recent randomized controlled trials (RCTs). Methods: A comprehensive literature search was conducted using databases including PubMed, Google Scholar, ResearchGate, ScienceDirect, and Cochrane Library. Inclusion criteria were randomized controlled trials (RCTs) that are written in english, published within the last five years, and involve game-based interventions. Exclusion criteria were if they are abstract-only, have been published less than five years ago, lack complete text availability, or are not written in english. sResults: Ten RCTs were reviewed involving diverse populations including stroke survivors,multiple sclerosis, brain injury, and patients with Alzheimer's disease. Most studies demonstrated improvements in cognitive domains such as memory, attention, processing speed, and executive functions. Enhanced outcomes were noted when CCT was combined with physical therapies (e.g., aerobic or vestibular training). However, several studies reported no significant differences between experimental and control groups, suggesting context-dependent efficacy. Limitations included small sample sizes, lack of long-term follow-up, limited generalizability, and concerns about real-world applicability. Conclusion: Lumosity brain training shows potential in cognitive rehabilitation, with positive effects on specific cognitive functions. The integration of CCT with physical interventions yield enhanced outcomes. Despite encouraging findings, further large-scale, long-term RCTs are needed to validate its efficacy and determine its advantages over traditional cognitive therapies.

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Neurorehabilitation, Lumosity Brain Training

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Blockchain technology has revolutionised the financial sector by allowing transactions to occur without governmental intervention. While traditional systems have governments controlling the flow of money through banks and financial institutions, blockchain uses a distributed ledger system that offers people additional security and anonymity. This essay looks at how governments control the financial sector and how blockchain threatens that authority. Blockchain reinforces financial sovereignty in two ways: on the one hand, control; and on the other, concerns about security, regulation, and misuse. Last but not least, this study investigates whether governments can control currency in a blockchain economy.

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Blockchain, Financial Autonomy, Decentralized Finance (DeFi), Government Regulation, Digital Currency Control, Privacy, and Security

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 Abstract

Study of Paintings of a Particular Region or Period (e.g., Mughal Paintings, Rajasthani Paintings, etc.)

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Study of Paintings of a Particular Region or Period (e.g., Mughal Paintings, Rajasthani Paintings, etc.)

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Sikh Dharam Ka Etihashik Adhayan (1469-1730)

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Sikh Dharam Ka Etihashik Adhayan (1469-1730)

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Abstract - In the present context, the academic audit is an important element in the Pupil Processing organization for assuring the quality of learning, research, Knowledge transfer and Identify, Whether they maintaining standard or not in the aspects of programmes offer and provide awards. It is an explicit provision of the institution to take responsibility for assuring quality of Teaching, Research and service. In any pupil processing organization, the Evaluation done continuously by having involvement in order to know the present position of academic activities and get remedy for that. The quality of education can be measured through academic audit by measuring the adequacy of academic input. It actually provides opportunity to implement the logical and meaningful steps to precede the academic activity in an effective manner.

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academic, audit, pupil, organization, learning, research, knowledge and quality.

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