Optimal Placement and Sizing of Renewable Distributed Generators for Power Loss Reduction in Microgrid using Swarm Intelligence and Bio-inspired Algorithms

To responsibly fulfill the world\u27s expanding electrical energy needs, renewable energy sources are now essential. Future energy policies must include these sources—like solar and wind energy—because they lower carbon emissions and save the environment. The optimal location and sizing of renewable distributed generators (OLSRDG) in the microgrid are determined in this study by applying one of the universal bio-inspired techniques and one of the swarms’ algorithms. With lower power losses, an improved voltage profile, increased dependability, and stability, the goal is to improve energy efficiency and lessen reliance on the main grid while also enhancing the grid\u27s overall performance and stability. The acquired results are promising and show the efficacy and resilience of the suggested technique in solving OLSRDG problems compared to recently published results. The results showed that the optimization process led to loss reduction, with the percentage of power loss reduction ranging from 45.387% to 73.89% using the PSO. While the percentage of loss reduction using the BAT ranged from 51.78% to 71.57%

Direct Torque Control of Dual Three Phase Induction Motor fed by Direct Power Control Rectifier using Fuzzy Logic Speed Controller

This paper presents an advanced Direct Torque Control (DTC) approach for a Dual Three-Phase Induction Motor (DTPIM) powered by a Direct Power Control (DPC) rectifier. Traditional control methods, such as Proportional-Integral-Derivative (PID) controllers, often face performance issues when motor system parameters vary or exhibit non-linearity. To tackle these challenges, we propose a fuzzy logic-based speed controller for DTC, which enhances adaptability to system dynamics without necessitating a precise mathematical model. The fuzzy logic controller (FLC) is particularly effective in regulating speed under varying load conditions, improving robustness, and minimizing torque ripple. Furthermore, the DPC rectifier enhances power quality by reducing harmonic distortions, maintaining a stable DC link voltage, and improving the power factor. Simulation results obtained using MATLAB/Simulink software demonstrate that the combined DTC-DPC approach with fuzzy logic control delivers a superior dynamic response with minimal overshoot. This framework offers a promising solution for high-performance industrial applications that require precise torque control and stability under fluctuating loads while also supporting sustainable energy practices through improved power efficiency

Uncertainty Quantification and Sensitivity Analysis of Concrete Structure Using Multi-Linear Regression Technique

The dynamic analysis of structures with uncertain parameters presents an attractive field of structural health monitoring in many cases of technological interest. In the dynamic analysis of hydraulic structures, such as existing dams, modeling assumptions, resulting inaccuracies, and changes in seismic loading are typically the main sources of uncertainties. Many hydraulic structures of concrete can be subjected to seismic loads. However, it is necessary to take haphazard or random phenomena as crucial considerations when assessing the security of these structures or planning new ones. This paper shows computational analysis for the characterization of the behavior of a concrete gravity dam under seismic loads, which are considered sources of uncertainties. The multi-linear regression methodology was performed and applied to evaluate the dynamic response of the considered structure. Numerous nonlinear time history analyses based on Latin Hypercube Sampling were realized to investigate the effect of uncertain parameters on the dynamic response. These analyses were applied to two types of seismic actions, the near and far earthquakes, which act on a concrete gravity dam. Then, a sensitivity analysis was used for each random variable to quantify its risk and clarify its influence on the dynamic behavior of the dam. Results divulge that for near-fault cases, major variables affecting the global sensitivity across all limit states are the Young’s modulus of soil and concrete. On the other hand, for far-fault cases, the important variables influencing the global sensitivity index include the compressive strength of concrete, Young’s modulus of soil, and cohesion

Identification of Plasma Proteins Associated with Alzheimer\u27s Disease Using Feature Selection Techniques and Machine Learning Algorithms

Alzheimer’s disease (AD) is a chronic, progressive neurodegenerative disorder that typically affects elderly individuals. Detecting Alzheimer’s using plasma proteins is a critical step toward improving treatment results for this disease. This study aims to use computational algorithms to explore the relationship between plasma proteins and AD progression by identifying a panel of plasma proteins that can serve as biomarkers for tracking and diagnosing AD. We applied two feature selection methods, Sequential Backward Feature Selection (SBFS) and Analysis of Variance (ANOVA) to extract significant proteins from a dataset of 146  proteins. The data was collected from the plasma of 566 individuals, comprising both Alzheimer’s patients and healthy controls. The SBFS technique generated all possible combinations of protein groups from the 146 proteins, which were then trained and tested using five machine learning models: Decision Tree, Random Forest, Extremely Randomized Trees, Extreme Gradient Boosting, and Adaptive Boosting. Subsequently, ANOVA was applied to refine and reduce the selected panel size. Finally, we used XGBoost and AdaBoost models to validate the final panel. The findings introduce a plasma protein panel consisting of A2Macro, BNP, BTC, PPP, and PYY proteins for diagnosing AD. This panel achieved a sensitivity of 88.88%, a specificity of 66.66%, and an AUC of 0.85. These results demonstrate that plasma protein biomarkers can facilitate timely interventions, potentially slowing disease progression and improving patient outcomes. This non-invasive and affordable diagnostic method has the potential to make Alzheimer’s screening accessible to a broader population

Odour, Colour and Turbidity Removal from Selected Industrial Wastewater Using Electrocoagulation Process

Electrocoagulation (EC) is an efficient electrochemical method for treating water using electric charges to destabilize and coagulate pollutants. In this study, a bipolar electrocoagulation reactor with aluminum electrodes was used to treat selected industrial wastewater. Key parameters, including odor, turbidity, Color, and other physicochemical parameters, were analyzed to evaluate the performance of the reactor. Focusing on textile wastewater and two types of cassava wastewater, fufu and starch, this study assessed Odor and turbidity removal using aluminum electrodes. Textile wastewater was used to examine Color removal. The operational parameters—voltage (10V–40V), temperature (30°C–38°C), and operating time (15 minutes to 1 hour)—were systematically varied to optimize the performance. The reactor significantly improved turbidity and color removal, with moderate odor reduction.  This study highlights the strong capability of the EC process in reducing turbidity using aluminum electrodes, despite challenges in Odor reduction. The electrocoagulation process effectively removed color, BOD, COD, TSS, and TDS from the wastewater. Voltage adjustments and electrolysis time are critical in optimizing pollutant removal and aligning with regulatory standards for industrial wastewater discharge. Despite its overall effectiveness, the challenges of achieving complete odor and BOD removal highlight areas for further research. This study highlights the potential of EC as a sustainable solution for industrial wastewater treatment

Malware Detection Using a Random Forest Method Trained on a Balanced Synthetic Dataset

The accuracy of malware detection is closely related to the available datasets, which are often small and imbalanced. To overcome these challenges, this study proposed a new method that creates synthetic malware data and increases the size and balance by generating several data sets with a flow-based model. Subsequently, a random forest classifier is fitted on this augmented dataset. This study aimed to analyze the generation of synthetic data based on flow-based models and the impact of synthetic data generation on the performance of a random forest for malware detection. A flow-based model was used to generate a balanced synthetic dataset based on the CICMalDroid2020 dataset. The generated data was used for feature selection and engineering to optimize the Random Forest model. The experimental results demonstrate the effectiveness of the proposed approach. The flow-based model generated an additional 13,402 samples, massively increasing the dataset size, even though the original dataset had only 11,598 data entries. After training on the synthetic augmented dataset, the Random Forest model achieved better performance compared to the original dataset evaluation with metrics precision (93%), recall (100%), balanced precision (96%), and the F1 score (91%). The results show that flow-based model-generated synthetic data can significantly enhance malware detection capabilities

Investigation of the Properties of Waste Expanded Polystyrene (EPS) Modified Bitumen

The "white pollution" caused by the indiscriminate disposal of waste-expanded polystyrene (EPS) packaging foams in Nigeria not only poses a serious environmental threat but also results in significant waste of resources. Again, the poor waste management practices adopted in most of the urban and rural areas in Nigeria also compounded this situation, coupled with the known fact that polymeric materials generally do not decompose (i.e., are non-biodegradable) easily, thereby making them serve as a threat to the surrounding environment. Given resolving this problem, these discarded EPS packaging foams were used in the modification of 60/70 bitumen in this study, as it has been reported that polymer modifiers are known as a possible solution for improving road life in the face of increasing and heavy traffic loading when suitably incorporated into the virgin bitumen. The binder physical tests were carried out on the virgin bitumen and the hot mix waste EPS-modified bitumen samples at modifier contents of 2.5, 5.0, 7.5, 10.0, 12.5, and 15.0%. The results from the tests showed that the best-improved properties were achieved at 5.0 % EPS modifier content with increased softening point (53°C), flash point (275°C), and decreased penetration (50 dmm), while its computed penetration index (-0.48) value indicated that this blend falls within the category of the most acceptable road bitumen. Waste EPS is a suitable bitumen modifier, which enhanced its performance and could equally solve the problem of “white pollution” in Nigeria

An Overview on Blockchain-based Social Media

Social Media (SM) platforms allow users to create and share multimedia content characterized by interaction among the users through profile creation, messaging systems, communities, etc. Blockchains can improve the security and ethical aspects of SM due to their innate security characteristics. We overlook countless blockchain-based SM schemes, where we perceive 9 duties of blockchain-based SM abstraction and dissect them intensively, rooted in SM- and blockchain-linked traits in contrast to existing reviews that do not review in broad scope and lack a critical analysis to show ways to focus on practical implementation. We heaped a preparatory sample of 93 works by sieving the studies for leaching rules and delving into E-repositories by employing a mixed-method systematic review with a narrative synthesis and quality assessment approach. Built upon the scrutiny, in blockchain-based SM, blockchain can assist by providing social media platforms (G1), social DApps (G2), copyright protection (G3), harassment prevention (G4), ensuring privacy and security (G5), fake/vicious news prevention (G6), data/user behavior/event processing and analysis (G7), proper incentivization (G8), and user migration (G9). Critical analysis uncovers that from blockchain-based social media, an overall 40% harness 20% from G6 and G7 each, 82.5% harness conventional blockchain, and 22.5% harness DPoS consensus. Next, we critically analyze the strengths and weaknesses of the reviewed works, focusing on performance and characteristics. Furthermore, we identified lack of empirical validation, under-exploration of ethical concerns and biases in detection models, and non-assessment of decentralization risks as gaps in the study. Eventually, we reveal the opportunities and discomforts of the abstraction of blockchain-based SM, state the study’s limitations, and then bestow solutions to resist them with future directions emphasizing practical implications

Fine-tuning AraGPT2 for Hierarchical Arabic Text Classification

Text classification consists in attributing a text to its corresponding category. It is a crucial task in natural language processing (NLP), with applications spanning content recommendation, spam detection, sentiment analysis, and topic categorization. While significant advancements have been made in text classification for widely spoken languages, Arabic remains underrepresented despite its large and diverse speaker base. Another challenge is that, unlike flat classification, hierarchical text classification involves categorizing texts into a multi-level taxonomy. This adds layers of complexity, particularly in distinguishing between closely related categories within the same super-class. To tackle these challenges, we propose a novel approach using AraGPT2, a variant of the Generative Pre-trained Transformer 2 (GPT-2) model adapted specifically for Arabic. Fine-tuning AraGPT2 for hierarchical text classification leverages the model\u27s pre-existing linguistic knowledge and adapts it to recognize and classify Arabic text according to hierarchical structures. Fine-tuning, in this context, refers to the process of training a pre-trained model on a specific task or dataset to improve its performance on that task. Our experiments and comparative study demonstrate the efficiency of our solution. The fine-tuned AraGPT2 classifier achieves a hierarchical HF score of 80.64%, outperforming the machine learning-based classifier, which scores 41.90%

Effect of Soil Salinity on Thermal Behavior of Sandy Soils Under a Sahara Climate

Soil salinity represents a harmful environmental problem that occurs either naturally or as a consequence of ineffective management practices by humans, especially in arid regions. Assessing ground temperature profile represents an important indicator for evaluating the performance of geothermal systems, used in air conditioning of building. This paper proposes a conceptual and numerical model for predicting the hydrothermal behavior of unsaturated soils. The effect of soil salinity was investigated in this research. Two soil types (sandy and sandy loam) were studied under three salinity levels. Moreover, the arid meteorological conditions of Sahara Desert were considered in the developed model. According to the results, it appears that sandy soils exhibit optimal thermal behavior at a moderate salinity concentration of C = 0.1 M, with increased surface temperatures during warmer periods and better heat conservation in colder conditions. Conversely, sandy loam soils respond most effectively at a higher salinity concentration of C = 0.2 M, particularly excelling in heat retention during the summer months. These results emphasize the intricate interaction between soil composition and salinity in regulating temperature patterns, providing important knowledge for enhancing soil management practices that are customized to particular soil types and environmental factors, with potential implications for geothermal applications