CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES

Optimal Parameters of Dynamic Absorber for The Vibrations of The Beam With Moving Dynamic Absorber

2025-11-05T10:21:07+00:00

Vibration control is of utmost importance in modern mechanical and structural engineering in which excessive oscillations can cause the failure of operations, noise, fatigue, or even the collapse of the structure. Dynamic vibration absorbers provide an effective, simple and energy-efficient solution for transferring vibrational energy of the primary system to the secondary subsystem, which reduces the amplitude of the main structure. Beams are highly common structures present in bridges, vehicles, robotics, and aircraft, which exhibit complex vibrations with dynamic or moving loads, such as that of a vehicle or a crane. Consequently, a moving DVA, which moves along the beam optimizing the suppression method is required, and with parameters such as mass ratio, damping, stiffness, and position liquidating the performance of the device on such system. Although DVAs are relatively well-developed, on beams with hysteresis-type elastic dissipative characteristics, under moving loads, improper tuning of the parameters can result in reduced functionality or even increase the vibrations of the structure at certain frequencies. Consequently, this study aims at finding such optimal parameters for moving DVA on a beam and absorber with hysteresis-type elastic dissipative characteristics using purely analytical methods. As a result, the optimization allowed to reduce the amplitude and energy transmission, the parameters for which were equal to 0.1253, 1.92 and 0.5 respectively. The results were obtained through analytical modeling, resonant analysis and the Den Hartog method, were at- and transmitted power were equal to zero, are used to find the transfer function equations and the invariant points of the system under kinematic and random excitations. Numerical analysis provided optimal mass, stiffness, and damping for the moving DVA on a clamped end steel beam. The amplitude-frequency characteristic results are highlighted in that transmission is limited in the vicinity of the resonant frequencies, and the curves are shifted to the right with absorber tuning when moving and the peaks are the lowest. The work is unique in its kind as it performs such optimization using the Ginzburg method and analytical methods in order to achieve optimal suppression rather than rely on the experiment only. Results of the study can be applied to optimize values of DVAs on hysteresis beam for vibration-sensitive structures like robotic arms and high-speed railways, in order to employ maximum suppressional capabilities of the device under dynamic excitations.

Mitigating Security Risks in Multi-Cloud Environments: A Blockchain-Enabled Zero Trust Architecture for Resilient Information Systems

2025-11-19T04:43:23+00:00

Multi-cloud computing environments are emerging as a standard in business settings but present much greater security challenges such as the lack of access control fragmentation, audit transparency, complexities of trust boundaries, and vulnerability to single point of failure due to centralized authentication systems. The article proposes a Zero Trust Architecture based on blockchain and the use of the Attribute-Based Access Control (ABAC) to have secure information systems in distributed multi-clouds. The suggested solution uses Hyperledger Fabric 2.5 and CouchDB state database to provide decentralized, immutable, and transparent access control by policy enforcement using smart contracts. Parallel transaction benchmarking was used to do overall performance testing on four test cases with 1,700 transactions with different concurrent loads (2-10 concurrent workers). The experimental data shows production grade level performance with the maximum throughput of 30.78 TPS, mean latency of 85.79 ms, and the best in class reliability (100% success rate). The system is highly linearly scalable with a 5.2x throughput increase between low and high load conditions, and surprisingly 80 percent reduction in the latency during heavier concurrent load conditions. It has made significant contributions, including: (1) production-quality ABAC smart contract deployment on modern blockchain platforms, (2) extensive performance testing by industry-standard parallel benchmarking methodology, (3) demonstration of successful practicability of real multi-cloud security deployments, (4) reproducible experimental setup with open-source deliverables, and (5) closing the theory-practice gap between theoretical blockchain-based access control models and realistic implementation that is suitable to security-critical enterprise settings where immutable audit logs and principles of Zero Trust are needed.

Modified Principal Points: A Flexible and Differentiable Approach for Data Summarization

2025-11-20T16:25:52+00:00

Principal points∗are a small set of characteristic locations which minimize the average squared Euclidian distance from the data points, and should be more informative about the data’s structure than simple features such as mean and variance. However it is also non-differentiable w.r.t point collapse due to minimum operation in (2.8) and weak-sparse in defining point spread penalty. In this paper we define the generalized principal points as the Gaussian weighted mean of distances. It results in a differentiable objective and has a tuning parameter for point closeness adjustment. When the bandwidth approaches to zero, modified points tend towards classical points from a real direction and when it tends to the infinity they define the mean. Finally, the simulation studies are reported and reveal higher robustness of our proposed methods against outliers, non-normality, and small sample sizes. Empirical studies on real statistical data also confirm that lower sensitivity to extremes is better

Utilizing The Time Series Model for Electricity Energy Consumption Prediction In Kirkuk Governorate, Iraq

2025-11-20T16:40:27+00:00

Correctness in the prediction of electrical consumption is essential as it allows for efficient utilization of energy without incurring excess cost or suffering from power outages. Many countries, including Iraq, have faced challenge in precise demand forecasting due to rising population, climate challenges and other factors. Despite several studies studying the energy prediction breake the time series models, but, in Kirkuk the dynamics of temperature, population growth and electricity consumption is not investigated in detail. The goal is to deploy VAR model for electricity consumption forecasting in Kirkuk while controlling for population and temperature influencing demand. The results present by the VAR model confirmed that population growth positively correlated with electricity consumption. This is especially handy because these two variables, energy demand and temperature, are interrelated over time. This study formulates a new forecasting model for Kirkuk, Iraq, using an advanced VAR (Vector Autoregressive) model with optimal lag order to produce the best possible forecast, particularly since large cities have their own special demographic and environmental factors. The results can provide for better management of the power grid which can inform the energy policies and infrastructural development in Kirkuk to better cater to the future electricity demands.

A Hybrid Movie Recommendation System Using Collaborative and Content-Based Filtering Techniques

2025-11-21T13:31:23+00:00

As the number of digital media content grows quickly, it is getting harder to find films that are timely and fit your tastes. This research describes how to develop and build a movie recommendation system that employs data filtering techniques to give each user customised movie suggestions. The system uses content-based filtering, collaborative filtering, or a combination of the two to look at user preferences and movie information. The algorithm uses user ratings, genre data, and viewing history to guess which films people will like and suggest them. This study looks at the system's design, algorithms, data pretreatment methods, and ways to measure performance. The implementation demonstrates that recommendation systems can provide significant value, sustain user interest, and enhance the entertainment experience. To see how accurate, scalable, and user-friendly the system is, a dataset from the actual world is used. To judge how good the ideas are, we look at performance metrics like precision, recall, and RMSE. Improvements in algorithms and hybridisation procedures help solve problems including scalability, cold start, and data sparsity. The article also talks about what will happen in the future, such as using deep learning models to make predictions more accurate, adding real-time recommendation capabilities, and integrating streaming services. Overall, the Movie Recommendation System looks like a good way to go at large media archives.

Ultra-Compact Cryptographic Engineering for the Internet of Things: Enhancing Security in Highly Constrained Environments

2025-11-26T16:34:39+00:00

This paper will design a cryptography framework intended to work within extremely lightweight and resource-constrained IoT implementation environments. Its goal is to design a secure and computationally efficient system that will be able to protect the large-scale IoT systems that support the critical infrastructure like healthcare systems, industrial sensors, and smart cities applications. The suggested architecture is a combination of algorithmic design, simulation, and hardware evaluation with the platform that has FPGA and ARM Cortex-M microcontrollers to evaluate the performance of the proposed architecture in real-world conditions. The model has been largely tested, showing substantial energy use (up to 40% of original power usage), increased throughput (up to 30% more), and lower latency (reduced by 20-50%) than more traditional cryptographic standards like AES and PRESENT. These findings establish lightweight encryption as viable with respect to protecting the IoT ecosystems without compromising the cryptographic strength. The area of this study puts its contribution in the context of the current digital transformation of Iraq, drawing on the framework of information-system compatibility of organizational readiness model. It is through such regional inferences that the paper suggests a framework of cryptography, which is engineering-oriented, enabling safe, energy-conscious, and scalable information exchange among national e-management systems. The results support the notion that ultra-lightweight cryptographic engineering can be used as a foundation to developing economies in order to support the development of secure, sustainable, and interoperable digital infrastructures.

Optimizing Network Routing and Resource Allocation using Deep Reinforcement Learning in Next-Generation Networks

2025-12-04T19:14:53+00:00

Next-Generation Networks (NGNs), including 5G and beyond, are characterized by unprecedented
scale, dynamism, and heterogeneity, posing significant challenges for traditional network management
paradigms. Static routing protocols and rule-based resource allocation mechanisms are increasingly
inadequate for handling the complex and time-varying traffic patterns inherent in these environments.
This paper proposes a novel framework based on Deep Reinforcement Learning (DRL) to jointly
optimize network routing and resource allocation. We formulate the problem as a Markov Decision
Process (MDP), where a centralized DRL agent learns optimal control policies by observing the global
network state, which includes link utilization, buffer occupancy, and quality of service (QoS) metrics.
The proposed model utilizes a Deep Q-Network (DQN) with experience replay and a target network
to ensure stable and efficient learning. The objective of the agent is to maximize a composite reward
function that balances network throughput, minimizes end-to-end delay, and ensures equitable resource
distribution. We conduct extensive simulations in a software-defined networking (SDN) environment
to evaluate the performance of our DRL-based approach. The results demonstrate that our framework
significantly outperforms conventional algorithms like Open Shortest Path First (OSPF) and a standard
Q-learning-based approach, achieving up to a 25% reduction in average network delay and a 15%
increase in overall throughput under high traffic loads. The findings confirm the potential of DRL to
enable intelligent, autonomous, and adaptive control in future communication networks.

Environmental Applications of Cold Atmospheric Plasma: Wastewater Treatment, Pollutant Degradation, and Air Purification

2025-12-05T07:03:16+00:00

Urbanization, agricultural practices and industrial waste have contributed to all environmental pollution, which has become a great concern worldwide, especially when it comes to this air quality aggravated, waste water pollution and constant organic pollutants. Traditional therapeutic techniques, including absorption, ozonation and chlorination, are often associated with high energy costs, inadequate environmental toxins and the production of dangerous underpaths. Given this, cold atmospheric plasma (CAP) has gained popularity in the form of a non-thermal, adaptable and durable technology that can produce a variety of reactive oxygen and nitrogen species (RON) for effective processing. With air purification, wastewater treatment and use of the use in frequent pollutants, this article provides a complete summary of CAP's environmental applications. Advanced oxidation procedures driven by reactive species with short and expanded half -life include the discussion of underlying plasma macology that removes contaminants. In addition, the recent CAP integration is emphasized with hybrid systems as a means of improving efficiency and reducing energy consumption in CAP integration. Relevant problems are strictly investigated, such as security problems, economic viability and scalability. Lastly, future prospects regarding CAP's potential as a game-changing instrument for environmentally sustainable management are discussed.

Efficiency of Bayesian Estimator of Shrinkage by Weighted Loss and Exponential Linear functions for Frecht`s Distribution by Using the Simulation Method

2025-12-06T11:26:00+00:00

This work deals with study of Bayesian shrinkage for parameters of the Frecht`s distribution by following two kinds of loss functions. These are weighted loss function and linear exponential loss function. The simulation method is depended as main manner using the Stata17 program to evaluate the efficiency of the estimators and to find the best Bayesian shrinkage estimator. Bayesian estimators have been derived and then both estimators were calculated by using standardized performance measures to make multi-condition simulation data including six different paths for feasible simulation. Each path checks a different aspect for an effect of sample size and calculates the estimators of the weighted and linear exponential loss functions. After that, a comparison between them is made aiming the arrival to a best estimator.

Real-Time Trajectory Tracking Control for UAVs via Online Genetic Algorithm-Based PID Optimization

2025-12-11T19:16:22+00:00

This paper presents a real-time trajectory tracking control strategy for unmanned aerial vehicles (UAVs) based on Online Genetic Algorithm (OGA) for proportional integral derivative (PID) controller optimization. The designed OGA-PID system adaptively changes controller gains during flight, providing robust adaptation against disturbances, noise, and changing operating conditions. A nonlinear UAV system was simulated under several reference trajectories, such as circular, figure-eight and helical trajectories, with performance measured through Monte Carlo experiments. Comparative results versus a baseline fixed-gain PID controller show that the OGA-PID method greatly minimizes tracking error of the X, Y and Z axes while providing smooth control input. Quantitative indices of performance, ISE, IAE and ITAE, also affirm systematic improvements of all trajectories. In addition, adaptive gain evolution during OGA updating reveals the capability of the algorithm to adjust control parameter settings at real time. the developed approach improves UAV trajectory tracking performance and robustness and presents a viable system framework for intelligent adaptive flight control.

A Mobile Net-Driven Deep Learning Framework for Accurate Dog Breed Classification

2025-12-12T05:29:49+00:00

Honeypots are strong cybersecurity tools that are meant to draw in and study bad behaviour in a safe and regulated setting. This study shows how to set up a low-risk honeypot system using Kali Linux, Oracle VirtualBox, and Python to watch for, record, and analyse possible cyberattacks. The system combines scripted interactions and automatic logging to mimic weak services. This lets it gather extensive information on unauthorised login attempts, port scanning, and attacks that leverage exploits. When you run everything in a virtualised environment, it makes sure that everything is well-isolated, keeps things from being accidentally exposed, and lets you keep an eye on things in real time without hurting the host environment. After data is collected, Python-based methods for analysing and visualising it are used to find patterns in attacks, trends in behaviour, and possible new threats. The results show that lightweight, scripting-based honeypots are a good way to raise security awareness and find threats early on because they are cheap, easy to maintain, and effective. This method shows that even simple honeypots can greatly improve defensive capabilities by giving information about how attackers act and making the entire cybersecurity posture stronger.

Challenges of E-Learning in The Era of Artificial Intelligence

2025-12-13T22:10:23+00:00

The growth of Artificial intelligence in education (AIED) is poised to influence the future of online education, educators and students as well as administrators and policy makers. Traditional e-learning is being revolutionized by AIED systems, with new challenges emerging from the effects of these technologies. Issues about funding, privacy, security and policy will reemerge as AIEd is tactically deployed. Further, the potential benefits of AIED for education can be best realized if its development and introduction are meticulously planned. Key concerns related to funding, data privacy, algorithmic transparency, security, and policy governance are resurfacing as institutions move toward strategic implementation of AIED technologies. These issues highlight the need for robust regulatory frameworks and ethical guidelines to ensure that AI applications support equitable and responsible learning experiences. Moreover, the potential benefits of AIED—ranging from personalized learning pathways to scalable support for diverse learners—can only be fully realized when their design, development, and deployment are thoughtfully planned and grounded in pedagogical principles.

Pass Man: A Modern Cloud Password Management System with Robust Encryption and User-Centric Security

2025-12-22T12:49:27+00:00

Pass Man is a modern password management solution that runs in the cloud and is designed to address the growing challenges of storing and managing secure credentials. As people use more digital services, it might be hard for them to keep strong, unique passwords across all of their devices. Pass Man protects user data by using end-to-end encryption and a zero-knowledge architecture. This means that even system administrators can't see or access user data. Each stored password is protected by a different key, which users can download as a key image. This adds an extra layer of security. The system lets you add, view, and edit passwords only after the keys have been successfully verified, ensuring that everything is real at every step. Pass Man was made with Flask, HTML, CSS, JavaScript, and Lottie animations. It has a user-friendly, responsive UI and solid security features. In the future, Pass Man will add browser extensions and a subscription-based model to reach more people and organisations, making it a complete digital security solution.

Big Data Analysis to Inferring Nationality Using X Social Network without GPS

2025-12-25T13:33:17+00:00

Inferring user’s nationality from social media destinations gets to be a hot inquire about topic. In this paper we propose a modern and basic data analysis and algorithm to predict the nationality of X social network client without utilizing any GPS data like past proposed algorithms. The proposed algorithm employs the X social network user friends location data as it were. In spite of the fact that as it were around 30% of the X clients compose their location data in important form, we demonstrate that this percent is sufficient to de-cide the root nation or the nationality of any X client. Our proposed algorithm classifies more than 90% of the X client in our collecting dataset. We utilize within the proposed algorithm six nations but this work can effectively be generalized to incorporate all the world nations.

Ensuring The Safety of Cloud Computing, A Trusted Cloud Service Framework

2025-12-26T23:02:35+00:00

Cloud computing became critical for modern information systems, giving configurable computing ability to users via the internet. While cloud computing provides scalability, elasticity, and cost efficiencies, challenges remain regarding the cloud-based data. This paper proposes the Trusted Cloud Service Framework (TCSF) with multi- authority weighted attribute-based encryption (W- ABE) and symmetric encryption using AES-256 and a conjoined sanctioning scheme with a Central Authority (CA) and Weighted Attribute Authority (WAA). The framework attempts to provide a solution to various concerns regarding Multi-Tenant Trust, Identity Management, and Access Control Data Security. The authors implemented a prototype system in Java and the authors provide Experimental Data Improvement in Encryption on Throughput, Improvement in Metric Resource Consumption, and Improvement in Being Resistant to Insider Threats, Collusion (i.e.). Based on the obtained empirical data, the authors show that TCSF is better than other systems, baseline CP-ABE and HABE systems, and remains effective for cases where the system needs to be used for online cloud data. The results obtained of the TCSF in cloud computing protections show the TCSF system provides a solution that is effective and increases the security of the system for users. Cloud TCSF provides an effective moderate system in data centres.

Geometric Factorization in ℤ[????]: A Graded Newton Polytope Algorithm for Multivariate Polynomial Decomposition

2025-12-31T02:39:53+00:00

This paper introduces the Graded Newton Filtration Algorithm (GNFA), a novel, deterministic, and geometrically grounded method for the exact factorization of multivariate polynomials with integer coefficients. The inherent computational difficulty of distinguishing prime (irreducible) from composite (reducible) factors in has long been dominated by classical algorithms like Zassenhaus, which suffer from exponential complexity due to combinatorial subset enumeration, and the LLL algorithm, which, while theoretically polynomial-time, is impractical due to large constants and numerical instability. GNFA essentially sidesteps these bottlenecks, by using the combinatorial geometry of the Newton polytope of the polynomial. The algorithm defines a graded ring structure on and encodes the global factorization problem in terms of local factorizations over the faces of through face valuations. Then, these local factors are such raised to a global divisor by LLL in fashion ensured by a Hensel- type lifting theorem in the associated graded ring. Under the Extended Riemann Hypothesis (ERH), GNFA enjoys a quasi-linear expected running time of, where is the number of non-zero terms, is the degree, \(d\) is the number of variables and bounds the coefficient bit-length a significant asymptotic improvement over all previous deterministic approaches. Extensive experimental comparison against state-of-the-art systems (Magma and SageMath) shows GNFA’s practical superiority by more than two orders of magnitude for benchmark classes including sparse polynomials, Vandermonde determinants, and Swinnerton-Dyer polynomials. The algorithm’s impact extends to critical applications: in post-quantum cryptography, it provides an efficient tool for certifying the irreducibility of Ring-LWE modulus polynomials; in algebraic coding theory, it facilitates the construction and cryptanalysis of Goppa codes. This work thus establishes a new geometric paradigm for polynomial factorization, setting a higher standard for exact computation in polynomial rings over the integers.

The Effect of Social Media Promotion on Consumer Purchase Behaviors

2026-01-02T11:03:10+00:00

The increasing popularity of social media has changed the manner in which consumers respond to products as well as their purchasing behavior. Currently, a large number of people make use of social networking sites like Facebook, Instagram, TikTok, and Snapchat to market their products, thereby impacting the buying decisions of consumers. At the same time, the popularity of e-commerce platforms, especially Amazon, has been on the increase in Baghdad. This has not been the subject of research despite its impact on buying behavior, as this research aims to investigate the impact of social networking on the buying decisions of Baghdadian Amazon users. The questionnaire was administered to around 150 respondents in order to explore the impact of social networking on the respondents' buying behavior. This analysis examines relationships between social media marketing promotion, customer trust, purchase intentions, and buying behaviors through multiple regression analysis, correlation analysis, and descriptive statistics. The empirical findings show that advertising on social platforms positively impacts purchase intentions and buying behaviors; this is largely attributed to customer reviews, social promotion discounts, and endorsement by social influencers. However, platform trust plays an important role in influencing relationships between social marketing promotion and buying behaviors on these online platforms. Other key factors that mitigate this impact or reduce its significance are demographic elements namely age, gender, and income levels in this research study. In this context, it can be concluded that social influencers are an important aspect for social platform marketing that must be addressed by online sellers if they are seeking to maximize social platform marketing impact in locations such as Baghdad.sales.

Machine Learning–Enhanced Metaheuristic Optimization for Nonlinear Problems: A Comprehensive Critical Review

2026-01-02T15:50:30+00:00

Nonlinear optimization problems are commonplace in multidisciplinary applications across engineered systems, energy, transportation, healthcare and computational intelligence. Many of these problems remain unsolvable, characterized by nonconvex search landscapes, multimodalities, high dimensionality, and complex constraints. Traditionally, metaheuristic approaches ranging from genetic algorithms to particle swarm optimization to ant colony optimization and differential evolution supply reliable performance but are ultimately ineffective due to slow convergence, premature convergence, or variances of solutions based on problem type. Yet with the latest trajectory of machine learning (ML) technologies, hybrid frameworks provide coupling agents from predictive modeling to adaptive learning to surrogate modeling to reinforcement-based decision support systems that realize enhanced performance by promoting quicker searches. This paper details a comprehensive and critical literature review of ML-coupled metaheuristics for nonlinear optimization. Findings compare recent developments to associated trends, categorize coupling frameworks, review performance increase percentages, acknowledge existing gaps, and recommend future research focus. Ultimately, ML-based metaheuristics promise a new frontier in top-level performance for nonlinear solutions; however, to make them as good as they can be, standardized benchmarking, increased explainability, and better theoretical justification are needed.

A Post-Quantum Cryptography Framework for Securing Banking Communications in Iraq

2026-01-04T06:00:53+00:00

Iraq’s banking sector has grown very fast in recent years, with over 18.5 million bank cards and electronic payments above 2 trillion IQD each month by 2023. While this growth improves financial access but it also increases exposure to cyber threats, which are rising worldwide. Iraq’s system of seven state owned and more than sixty private banks shows uneven cyber readiness, with private banks facing greater challenges. In addition, quantum computing threatens existing encryption methods like RSA and ECC. This paper analyzes Iraq’s banking cyber risks and proposes a post-quantum cryptography (PQC) model to strengthen security, support both public and private banks, and prepare for future quantum-safe standards.

Some Estimation Methods for Spatio-Temporal Data in Spatial Statistics: (A Comparative Study)

2026-01-08T04:33:51+00:00

Traffic accident fatality is a pressing public health issue that demonstrates significant spatio-temporal heterogeneity and would benefit from a statistical approach that can appropriately account two concurrently. In the field of spatial statistics, spatio-temporal regression models and kriging methods are used for estimation and prediction, but their relative performance varies largely according to data structure and the availability of explanatory covariates. In addition, there is quite limited empirical evidence that compares these two approaches to their application over real spatio-temporal traffic fatality data, specifically for developing countries, and in contexts where multiple covariates are available. A major aim of this study is to compare the efficiency of estimation using spatio-temporal linear regression and spatio-temporal ordinary kriging to model the total number of traffic accident fatalities in 15 provinces in Iraq during the period 2020–2023. The results demonstrate the superiority of the spatio-temporal regression model over kriging in prediction error and explanatory power by effectively integrating road, vehicle, driver, and pedestrian variables. The work we presented provides a focused, data-rich comparison showing that for fatality data with relevant covariates, regression based spatio-temporal modeling outperforms dependence on spatial temporal dependence. The observed patterns indicate that traffic safety analysis and policy should focus on models that incorporate explanatory variables, while future methodological decisions should be based on the underlying concept, data availability, and the strength of covariates of interest.

Energy Consumption Prediction in Smart Homes Using Machine Learning and Deep Learning Approaches

2026-01-15T14:07:00+00:00

The intelligent control of smart homes for energy savings and sustainability relies on accurate predictions of energy consumption. Many machine learning (ML) and deep learning (DL) models currently exist for this purpose; however, systematic investigations into their predictive performance, computational requirements, and cross-dataset validity are limited. This study proposes a household energy prediction benchmark to assess classical ML solutions (Support Vector Machine, Random Forest, Gradient Boosting), DL alternatives (Artificial Neural Networks, Long Short-Term Memory, Gated Recurrent Units), and a combined CNN-LSTM framework. Validations were performed using two of the most cited smart home datasets, REFIT and UK-DALE, for generalizability across homes with varied sampling resolutions. Assessments were made according to prediction effectiveness (RMSE, MAE, MAPE, and R²) and computational demand. The findings show that DL models outperform classical models, CNN-LSTM outperforms the other homogeneous networks tested on both datasets, and a robust analysis supports hybridized convolutional feature extraction and recurrent temporal modeling as superior to more straightforward alternatives. Finally, a discussion of the advantages of CNN-LSTM compared to the associated costs of its IoT-enabled smart home implementation indicates that energy consumption forecasting is necessary to assess the potential of peak load consumption and bolsters the call for sustainable metropolitan energy infrastructure systems.

A Secure Distributed File Storage Framework Using Cryptographic Hashing and PoW

2026-01-21T23:16:54+00:00

The growing concerns around data security and privacy have highlighted the need for secure, decentralised file storage solutions. This project proposes a Blockchain-Based Secure File Storage System that leverages blockchain technology to ensure the integrity, confidentiality, and immutability of files stored on a distributed network. In this system, files are securely stored by associating them with cryptographic hashes within blocks, creating a tamper-resistant ledger. The Blockchain is responsible for tracking file metadata, such as file names, owners, and timestamps, while ensuring that each file is securely linked to its predecessor, forming a chain of blocks. Each block's validity is maintained using Proof of Work (PoW), ensuring consensus and the integrity of the data stored. The code implementation allows users to add files, store them securely, and verify their integrity via a decentralised ledger. As files are uploaded, they are hashed, and a new block is created on the blockchain. The use of Proof of Work ensures that only valid blocks are added to the chain, preventing malicious tampering or unauthorised access. This system is ideal for scenarios requiring high levels of data security, such as cloud storage, healthcare data management, and legal document storage, where the integrity and authenticity of files need to be guaranteed over time. This project demonstrates how blockchain technology can be adapted beyond cryptocurrency to build a secure, trustworthy file storage framework that offers enhanced protection against data breaches and unauthorised modifications.

Article Review: Electromagnetic Pollution Resulting from Non-Ionizing Radiation in Kirkuk City

2026-01-24T14:03:26+00:00

A new environmental threat on a global scale is electromagnetic pollution, which is caused by non-ionizing radiation (NIR). Using emissions from mobile cellular base stations and high-voltage transmission lines as a focal point, this paper examines the levels of non-ionizing electromagnetic radiation in Kirkuk City, Iraq. Research based on measurements shows that EMF levels in residential areas are still below the recommendations made by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). It is recommended that ongoing monitoring and public awareness be implemented to minimize the long-term impacts of exposure, even though the acute health hazards seem to be minimal.