Sustainable Engineering and Innovation

Sustainable urban heritage strategies for Iraq's holy cities: A case study of the Old City of Najaf

2026-02-27T09:34:32+01:00

Najaf is a preeminent historical sacred city, hosting millions of secular visitors annually. However, it suffers from chronic heritage management inefficiencies due to the lack of context-sensitive integrated systems. This study innovatively synthesizes the Historic Urban Landscape (HUL) approach with Sustainable Development Goals (SDGs) to redefine urban heritage in Old Najaf as a liveable, adjustable landscape rather than mere historical remnants. By focusing on the historic center, the research presents a framework for landscape-based planning tailored to sacred sites with deep religious and political resonance. Specifically, the study aligns urban interventions with SDG 11 (Sustainable Cities and Communities) to establish a measurable route for integrated heritage plans. Drawing on expert consensus, three sustainable development models were identified, integrating UN Principles, SDG 11 indicators, and UNESCO’s HUL aspects. These models provide a strategic balance between modern social infrastructure needs and the preservation of sacred historical identity, offering a replicable blueprint for similar global sacred contexts.

Morlet wavelet–based olfactory-evoked EEG features for random forest classification of normal, aMCI, and Alzheimer’s disease

2026-01-11T12:51:34+01:00

Olfactory impairment and abnormal frontal EEG oscillations are recognized as early markers of Alzheimer’s disease (AD). Using a publicly available olfactory EEG dataset of 35 subjects spanning normal cognition, amnestic mild cognitive impairment (aMCI), and AD, each with MMSE scores and demographics, stimulus-locked epochs from four electrodes (Fp1, Fz, Cz, Pz) were processed with wavelet-based time–frequency analysis. Band-limited power ratios (delta, theta, alpha, beta) were computed as log-transformed post-odor/baseline values and aggregated to subject-level features. Statistical analyses revealed graded attenuation of odor-evoked frontal (Fp1) band-power ratios across groups, with significant differences in several band–odor combinations. PCA of Fp1 features showed partial separation of diagnostic categories, while multi-channel features offered weaker discrimination. Random forest classifiers trained on Fp1-only features achieved 66.7% test accuracy, outperforming the four-channel model (55.6%), with moderate sensitivity, specificity, and precision. These findings highlight that compact frontal wavelet-derived band-power ratios during olfactory stimulation carry diagnostically relevant information for distinguishing Normal, aMCI, and AD. The transparent pipeline, combining time–frequency processing, subject-level aggregation, and multiclass classification, offers a scalable framework that can be extended to larger cohorts or integrated with multimodal biomarkers.

Hybrid AI model-driven dynamic spectrum sharing for 6G wireless IoT networks

2025-12-23T09:21:09+01:00

The immense scale of the Internet of Things growth in 6G is utterly inconceivable to address utilizing conventional static spectrum allocations. A paradigm shift towards dynamic spectrum sharing is necessitated. In this article, a hybrid artificial intelligence model that combines deep reinforcement learning and a blockchain-based distributed consensus engine has been presented. Intelligent, secure, and efficient spectrum sharing may be accomplished using our model. The proposed methodology employs multi-agent reinforcement learning for efficient decentralized decision-making and IoT-enabled spectrum utilization. Specifically, IoT devices can use MARL to dynamically determine their power budget or spectrum resources to avoid inducing or experiencing interference while delivering acceptable quality of service. Using a blockchain engine to record and validate spectrum transactions enables transparent security in spectrum access. Our proposed hybrid AI model may be used to improve spectrum efficiency by 35%-40% while lowering energy usage by around 30% via intelligent sleep-wake lexicography methodologies and decision predication relative to traditional 5G. We thoroughly covered the spectrum management topic in 6G-IoT, demonstrating the feasibility of AI-based solutions.

The application of blockchain technologies in information security and computer systems data

2026-01-12T23:17:15+01:00

The rapid expansion of digital systems and the increasing frequency of cyberattacks have made information and data security a critical global concern. This challenge is particularly severe in Ukraine, where prolonged conflict with Russia has involved hybrid warfare, including persistent cyberattacks on digital and information infrastructures. This study examines the use of blockchain technology to improve secure data management through an intelligent Hybrid Blockchain–Relational (HBR) architecture. Sensitive data are stored on a private blockchain (Hyperledger Fabric), while less sensitive data are maintained in a relational database (PostgreSQL), with data integrity ensured through Merkle root anchoring. A simulation using Ukraine’s Land Cadaster data served as the case study. Under Byzantine fault and system degradation conditions, Blockchain-based Consensus Optimization (BRCO) achieved a 40% reduction in transaction completion time and a 66.7% increase in node fault tolerance compared to Practical Byzantine Fault Tolerance (PBFT). The proposed HBR+BRCO design demonstrated low latency (50 ms), efficient resource usage, and a throughput of 500 TPS, highlighting its effectiveness and real-world applicability.

Detecting spatial and temporal myopia using machine learning algorithms

2026-02-09T17:20:12+01:00

This study aims to examine the ability of machine learning algorithms to detect strategic myopia in organizations. As it consists of two variables, the first machine learning algorithms as independent variable with two dimensions: Decision trees classification and K- Means clustering, while the second variable is strategic myopia as dependent variable with two dimensions: spatial and temporal myopia. This study adopted a quantitative approach, and a publicly available HR dataset obtained from Kaggle was used to ensure data privacy. The dataset, which has been used in this study, represents the organizational internal factors with 14,999 employees’ records. Both decision trees and K-means were applied to the internal factors’ datasets, showing the likelihood of employees staying in the organizations and clustering the customers into three clusters. The study revealed that both decision trees and k-means can help organizations in detecting spatial and temporal myopia, and the researchers recommended that organizations should integrate machine learning algorithms in their decision-making processes.

A computer vision as a tool for automated quality control in smart manufacturing

2025-11-05T13:24:01+01:00

Computer vision (CV) has emerged as one of the most significant enablers of intelligent factoring system quality control, automated in the context of the AI revolution in the industrial setting today. In this research, we discuss how CV-based architecture can be applied to achieve real-time, adaptive, and scalable quality assurance. This is new research because it is an amalgamation – the evaluation of different mathematical models and artificial intelligence (AI). Deep learning, transfer learning, Bayesian networks, and edge computing are among the solutions, as are fog-cloud partnerships and their direct impact on manufacturing output, productivity, and decision-making efficiency. The article provides comparative data on the performance of other CV frameworks in different industrial conditions by critically examining the new case studies. The practical implications are recommendations for adopting vision-driven systems to improve product consistency, increase human-machine interaction, and reduce operational downtime. In addition, the paper identifies shortcomings in computational resources, system compatibility, and information security that should be addressed in the next generation of smart factories.

Estimating survival rates using artificial intelligence combined with the Aalen–Johansen estimator in multi-state models

2025-08-16T15:41:22+02:00

Accurate survival prediction is essential for clinical decision-making, health economics, and treatment planning. Traditional methods like the Kaplan-Meier and Cox models are widely used but have limitations when applied to complex multi-state processes or individualized predictions. The Aalen–Johansen estimator, a non-parametric approach suited for multi-state Markov models, improves population-level inference but lacks the ability to incorporate covariates or capture nonlinear relationships. In this study, we propose a hybrid framework that combines the Aalen–Johansen estimator with artificial intelligence (AI) techniques, specifically gradient boosting machines (GBM) and long short-term memory (LSTM) networks. By transforming transition probabilities into subject-level pseudo-observations, AI models can learn personalized survival functions based on individual covariates. We validate our approach on both simulated and real-world clinical datasets. The hybrid model outperforms traditional estimators in predictive accuracy, as measured by calibration and discrimination metrics such as Brier score and area under the curve (AUC). This AI–Aalen–Johansen framework enhances risk stratification and clinical decision-making by providing more accurate, scalable, and interpretable survival predictions. Our results support its potential as a valuable tool in modern healthcare analytics, contributing to the advancement of precision medicine.