This study seeks to design an intelligent system for face expression classification through the application of deep learning methods. Face images are analyzed to extract salient features and pattern them into facial expressions like happiness, sadness, anger, surprise, disgust and fear with a neutral face expression. A Convolutional Neural Network (CNN) framework was used because it is very effective in the analysis of images and in recognizing patterns. The model is trained from typical facial expression databases and tested with performance indicators like accuracy and error rate. The outcomes of the study reveal that the proposed model is highly accurate in the classification of feelings and it is therefore useful in the realistic applications of human-computer reciprocations, security and monitoring systems and in the care of mentally ill individuals.

Recycled concrete aggregate (RCA) in self-consolidating concrete (SCC) has the potential of reducing the environmental footprint and financial cost of this type of concrete- a benefit that is all the more relevant. The purpose of the project is to prepare SCC where the traditional coarse aggregate will gradually be substituted by recycled coarse aggregate in replacement rates of 00 per cent, 25 per cent, 50 per cent, 75 per cent and 100 per cent. SCC is very high workability: it flows and fills formwork self-underlying, without any mechanical vibration, which contributes to the elimination of numerous on-site placement difficulties. The homogeneous SCC mixture cannot be achieved easily; however, SCC is very sensitive to ambient conditions, constituent material and mix proportions. This paper was dedicated to the issues of producing such concrete, which were evaluated mainly through slump-flow test. A total of 15 mixes were prepared with different conditions, which included five levels of RCA replacement and three volume fractions of steel-fiber (0, 0.5 and 1.0). The research problem examined is the possibility of reusing the RCA of the construction debris and broken concrete. RCA is used in place of natural coarse aggregate at various levels to identify the best replacement ratio. Simultaneously, different volumes of steel fibers are added and their interaction with RCA is analyzed with the aim of enhancing the flexural behavior of SCC. The results of fresh-state tests indicate that workability is lower when the level of RCA content is higher; the introduction of steel fibers also lowers flowability and passing capability. To a degree, the substitution of natural coarse aggregate with RCA increased flexural. Besides, the flexural strength of SCC was enhanced by steel fiber incorporation.

Adaptive QAM is critical scheme that provides communication systems with the ability to adapt to the varying channel fluctuations and preserve an acceptable level of spectral efficiency. In order to further enhance the adaptive QAM performance, odd-bit cross QAM modulation schemes had long been suggested as a candidate to improve the adaptive QAM transmission efficiency of modern communication systems like SC-FDMA and OFDM systems. The superior performance of cross odd bit QAM in terms of bit error rate (BER) and peak to average power ratio (PAPR) compared to the rectangular odd-bit schemes had made it the best choice whenever an odd-bit modulation scheme implementation is intended. Though many researches and articles extensively studied and analyzed the performance of odd-bit QAM schemes for various communication systems, BER performance under the simultaneous presence of additive white Gaussian noise (AWGN) and phase noise (PhN) impairments for odd-bit cross QAM based communication systems caught almost no research interest. In this paper, the BER performance for SC-FDMA system with odd-bit cross QAM is investigated with the simultaneous presence of AWGN and phase noise for various modulation sizes. The results shows that cross odd-bit QAM schemes show better uncoded BER performance in lower modulation orders, while offering only marginal improvements at higher orders. For instance, at a phase noise standard deviation of 10°, 8-QAM achieved a substantial logarithmic BER gain of 8.7 dB over 16-QAM, whereas 128-QAM showed a modest gain of just 1.4 dB compared to 256-QAM.

This research aims to study the role of project management development in improving organizational work and enhancing organizational performance within modern organizations. The results showed that the development of project management is positively associated with improving organizational insights and raising the efficiency of operations, which contributes to enhancing the quality of strategic decisions and achieving the organization's goals more efficiently. The research relied on a descriptive-analytical methodology using questionnaires directed to employees and managers, with a hypothetical statistical analysis that clarifies the relationship between the development of project management and improving institutional work. The results showed a positive and statistically significant effect of project management development on organizational performance.

Air quality within the Middle East has become a major environmental and public health crisis because particulate matter exceeds World Health Organization (WHO) acceptable standards. The construction of Middle Eastern air pollution temporal relationships leads to improved predictions through the implementation of Long Short-Term Memory (LSTM) neural networks. Our investigation examines 15 regional urban centers by analyzing more than 450 data points gathered across 30 days which includes environmental measurements together with detailed readings of PM2.5, PM10, NO2, O3, CO and SO2 pollutants. The proposed LSTM model implements state-of-the-art hyperparameter optimization through a three-layer (128 layers, 64 layers, and 32 layers) architecture combined with L2 regularization (0.001) and dropout regularization (0.3). The prediction model outperforms daily forecasts through its sliding window method which trains the network across seven consecutive days. The Root Mean Square Error (RMSE) of 23.39 μg/m³, Mean Absolute Error (MAE) of 19.90 μg/m³ and Mean Absolute Percentage Error (MAPE) of 30.07% for PM2.5 prediction results demonstrate that LSTM outperforms Linear Regression and Random Forest models. Fine particulate matter in the area exceeds 90% from human-made sources while showing seasonal and inter-city differences in the data analysis. The research solves critical technical deficits through extensive cross-validation procedures together with detailed baseline model assessments and comprehensive model design descriptions. The study demonstrates that LSTM networks successfully identify complex temporal patterns in Middle Eastern air pollution data for public health and environmental monitoring applications. The research presents a solid prediction system for arid and semi-arid areas with their complex weather patterns and elevated human-generated pollution while advancing environmental informatics knowledge base.

In this paper, a new method for solving some differential equations called the double Laolace-NO transform is presented. This technique is efficient in solving partial differential equations; several properties of this dual technique are introduced and the Laolace-NO transform of the basic functions is found. In addition, we apply this dual technique to find the exact solution of the Laplace and Poisson partial operator equations. The advantage of this technique is that it has three coefficients in addition to generality.

This study proposes a decentralized coordination framework for autonomous drones by integrating Multi-Agent Systems (MAS) with Swarm Intelligence (SI). Using simple local behavioral rules—separation, alignment, and cohesion—drones operate collaboratively to achieve shared objectives without central control. Through a series of simulated scenarios, the model demonstrated strong performance across key tasks. In the collision avoidance test, the system reduced collision incidents from 30 to zero within 10 seconds in high-density swarms. During goal convergence, over 80% of drones reached the target within an average of 12–18 seconds, depending on swarm size. For spatial distribution, the swarm achieved up to 96% area coverage with minimal inter-agent spacing. Even under stress tests like GPS drift, agent loss, and communication failures, the swarm recovered in less than 7 seconds. These results confirm the scalability, fault tolerance, and adaptability of the model, making it suitable for real-world applications such as search and rescue, environmental monitoring, and precision agriculture.

The open nature and resource limitations of Wireless Sensor Networks (WSNs) make them more exposed to numerous cyber threats. IDSs function effectively as key components for securing such networks. The research introduces a new deep learning intrusion detection system that integrates IGOA with CNN to enhance WSN intrusion detection capabilities. IGOA automatically optimizes CNN classification performance through an improvement of both accuracy and generalization capabilities. The proposed method demonstrates superior performance in WSN-DS dataset experiments since it reaches 99.94% accuracy that exceeds several state-of-the-art methods. Multiple assessments of precision, recall, F1-score and confusion matrix analysis and ROC-AUC metrics prove that the model can deliver reliable performance in real-world applications.