This research presents a comparative experimental study of machine learning models for botnet attack detection in Internet of Things network using N-BaIoT dataset. The dataset consists of benign traffic and malicious traffic generated by Mirai and BASHLITE botnets family. A total of 115 traffic features are used for classification. The study compares three representative models of learning, namely Random Forest as a classical machine learning classifier, XGBoost as an advanced model of ensemble learning and fully connected Artificial Neural Network as a deep learning classifier. The evaluation of the models was done looking for performance metrics like accuracy, precision, recall, F1-score etc. As per th

The seasonal variation in the level of the river influences the quantity of water that these dams can contain. In this study, the effect of changing the water level of the river on the stability and performance of a filtration system at a small earth dam is evaluated for a period of 10 years. The article discusses the influence of alternate droughts and heavy rain periods on dam water storage and highlights that hydraulic conductivity is not constant, as is usually considered in leakage studies. Specifically, the study focuses on the Al-Wand Earth Dam. The study analyzed the daily water pressure data from 2015 to 2025 using GeoStudio (SEEP/W). The research was carried out in four steps: data gathering, computer modellin

This study examines two distinct methods for preparing zinc oxide nanoparticles (ZnO NPs): the chemical method and the green method. In the chemical method, zinc acetate and deionized water were used, while in the green method, the plant extract from the fruit of the Capparis spinosa plant was used. The nanoparticles of ZnO were analyzed using X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS), field emission scanning electron microscopy (FESEM), and Fourier transform infrared spectroscopy (FTIR). ZnO nanoparticles prepared by chemical precipitation and biological methods were characterized using UV-Vis spectroscopy to analyze the absorption spectra. The FESEM images revealed the spherical shape of the nanoparticles. The

Light atoms, such as oxygen, are treated with the 6-311G** basis sets, and heavy atoms, like tin (Sn), are treated with SDD (Stuttgart/Dresden) basis sets. A density functional theory (DFT) with a B3LYP hybrid functional calculation needs to be done to work out the geometrical and electronic properties, such as the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), and the energy gap, as well as the thermodynamic properties, such as Gibbs free energy, enthalpy, entropy, and heat capacity of the tin dioxide (SnO₂) pyramid nanocluster structure as a function of the number of oxygen atoms. These theoretical calculations were performed using Gaussian 09W, while the geometry was visualized using Gauss

In this study, polyvinylpyrrolidone (PVP) fibers were fabricated using electrospinning, and their properties were enhanced through dual doping with zinc (Zn) and tin (Sn) ions at different Zn:Sn mixing ratios. The structural, optical, and electrical characteristics of the fibers were investigated to evaluate their potential for photodetector applications. The X-ray diffraction (XRD) analysis of pure PVP fibers indicates an amorphous nature, whereas two broad diffraction peaks emerge after mixing with ions, which is caused by the rearrangement of PVP chains due to their interactions with the added ions. Field emission scanning electron microscopy (FESEM) images show a significant reduction in fiber diameters and an increase in the density

A comprehensive theoretical framework is developed to evaluate the anisotropic coherence length in high-temperature superconductors based on the fluctuation-induced conductivity approach. The theoretical model is formulated using well-established expressions derived from the anistropic Ginzburg-Landau theory, incorporating both Aslamazov-Larkin and Maki-Thompson contributions. Numerical calculations are carried out for two representative superconducting systems, namely nano-(Co_0.5Zn_0.5Fe₂O₄)_x/(Cu,Tl)-1223 and Cd-doped (Cu,Tl)-1234 phase, which exhibit distinct dimensional characteristics. The reduced paraconductivity is analyzed as a function of reduced temperature, and the extracted

Lead and concrete blocks are well known for shielding against ionizing radiation, but their use is limited due to their high density, toxicity, and environmental concerns. This study investigates the potential of Bambusa vulgaris (bamboo powder)-based mortar composites as an alternative gamma-ray radiation-shielding material. Different blends of bamboo powder, sand, and cement were chemically treated to produce binary and ternary composites. The elemental composition of Bambusa vulgaris was determined using X-ray fluorescence (XRF), while the radiation attenuation coefficients of the samples against rays were determined using a NaI(Tl) scintillation detector and Cs-137 gamma source (662 keV). Key shielding parameters, including Linear an

Early diagnosis and effective rehabilitation of cerebral palsy (CP) are essential for improving functional outcomes and reducing long-term complications. CP affects the ability to perform daily physical activities and is often associated with secondary health problems such as obesity and chronic pain. Owing to the complexity of the condition, rehabilitation typically requires continuous supervision by clinical specialists, which can be challenging in environments with limited resources. Recent advances in artificial intelligence (AI) have created new opportunities for enhancing CP healthcare. This review summarises the major applications of AI in diagnosis, clinical decision support, motion classification and rehabilitation systems. AI-b

In this connection, this paper proposes a Deep Q-Network (DQN) approach to address the dynamic nature of the job-shop scheduling problem. Dynamic scheduling requires an efficient and reliable algorithm for handling disruptions such as machine breakdowns and job priority changes. When comparing DQN with traditional approaches such as genetic algorithm (GA) and PSO, the latter algorithms cannot cope with the problems. On the contrary, DQN gains knowledge from its experiences within the factory and develops a strategy for solving the scheduling problem through minimizing makespan and maximizing machine utilization. Moreover, using experience replay (ER) and target networks enables DQN to maintain stability and develop an optimal schedule. E

Mesoporous alumina is a promising drug carrier because of its extensive surface area, pore structure, biocompatibility and non-toxicity, facilitating high drug loading and release capacity. This study aimed to investigate the adsorption and release kinetics of levofloxacin (LF) on prepared γ-mesoporous alumina nanoparticles (γ-MNPs). Adsorption kinetic tests were carried out by a batch method, drug loading was investigated using an impregnation method, and the release kinetics were determined using a dialysis bag maintained at 310 K. Adsorption kinetics were analysed using three kinetics models: the pseudo-first-order, pseudo-second-order and intraparticle diffusion. Results showed that the adsorption of LF followed a pseudo-second-ord

This work presents the fabrication of highly efficient gas sensors based on silver oxide (AgO)- doped cadmium sulfide (CdS) nanostructures deposited on porous silicon (PS) substrates using pulsed laser deposition (PLD). Three doping concentrations of AgO (5, 10, and 15%) were investigated to evaluate their influence on the structural, optical, and gas sensing properties of CdS. The prepared films were characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), atomic force microscopy (AFM), UV–Vis spectroscopy, and Fourier transform infrared spectroscopy (FTIR). The XRD results confirmed enhanced crystallinity with increasing AgO concentrations, while FESEM and AFM analyses revealed a nanostructure

The primary objective of this study is to investigate the effects of thermal curing and fly ash incorporation on the mechanical and physical properties of reactive powder concrete (RPC) to develop a more sustainable RPC mixture with partial replacement of ordinary Portland cement (OPC) by fly ash. Four mixes were prepared: a reference mix (R) and mixes with fly ash replacement levels of 10%, 20%, and 30% (RF10, RF20, and RF30). Each mixture was cast and tested in three specimens for each age of testing. The specimens were thermally cured at 70°C for 5 hours per day for a total of 3 days, followed by water curing until the testing age. The experiments showed that adding 20% fly ash improved the overall p

The effect of active-layer thickness on PM6:Y6 organic solar cells' performance was studied using several characterization techniques, such as current density vs. voltage (J-V), capacitance vs. voltage (C-V), charge vs. voltage (Q-V), charge extraction by linearly increasing voltage (CELIV), and sun-dependent. Results show that increasing thickness increased light absorption and therefore short-circuit current density (J_SC), but excessive thickness caused transport limitations and higher recombination, resulting in reduced fill factor (FF). On the other hand, open-circuit voltage (V_OC) remained weakly dependent on thickness variation. As a result, power conversion efficiency (PCE) has initially increased and then de

This work presents a numerical study of a multilayer optical biosensor based on distributed Bragg reflectors (DBRs) with a blood-filled cavity. The Transfer Matrix Method (TMM) was employed and simulated in MATLAB to analyze the resonant behavior over the wavelength range 780-810 nm. The analysis also considered the effects of temperature variation from 15 to 40 °C and changes in the silicon layer thickness from 20 to 200 nm. The results revealed a blue shift in the resonant wavelengths of the defect mode due to temperature variation, leading to a negative thermal sensitivity whose magnitude depends nonlinearly on the silicon layer thickness. In addition, electric field intensity distributions at resonance revealed stronger localization

When acquired or congenital defects need restoration, maxillofacial silicone is the material of choice. Silicone, nevertheless, is far from perfect in quality. The primary objective of this research was to examine the effect of adding yttria-stabilized zirconia nanoparticles (YSZNPs) (0.5 and 1% by weight) on the wettability, surface roughness, atomic force microscopy (AFM), and X-ray diffractometer (XRD) properties of maxillofacial silicone (VST-50) room temperature vulcanization (RTV). The data were analyzed statistically. The YSZNPs were introduced to the VST-50 RTV in percentages of 0.5% and 1% by weight. A total of 63 samples of VST-50 RTV maxillofacial silicone were prepared and tested. Each group was subdivided into three identica

This study investigates the photoluminescence properties of lead iodide (PbI₂) nanoparticles embedded in a polyvinyl alcohol (PVA) matrix when illuminated by ultraviolet (UV) light. The PbI₂ nanoparticles, dispersed uniformly within the PVA polymer matrix, exhibit enhanced stability and efficient light emission due to the protective environment provided by PVA. Upon UV illumination, the PbI₂ nanoparticles generate visible light, with emission characteristics influenced by the nanoparticle size, concentration, and interaction with the PVA matrix. The role of PVA as a stabilizing agent and its effect on the photophysical properties of PbI₂ are analyzed, showing an improvement in quantum efficiency and photostability. This hybrid

Metal-Organic Frameworks (MOFs) are essential in nanotechnology applications due to their unique properties. MOFs can be easily tailored for specific uses by altering the metals or organic linkers involved, such as zirconium-based metal-organic frameworks (Zr-MOFs). Among the several methods used to create nanoscale Zr-MOFs are microwave-assisted synthesis, solvothermal, sonochemical, ionothermal, and mechanochemical procedures. The synthesis, structure, and characterization of Zr-MOFs were compared in this project between conventional solvothermal and microwave-assisted methods for nitrogen adsorption/desorption. Using X-ray diffraction (XRD) analysis, Fourier transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA),

The study aimed to use beta-glucan extracted from barley to prepare eco-friendly silver nanoparticles (AgNPs) using green synthesis methods. The barley variety "Abah 99" was used, with an extraction yield of 78% and a concentration of 4.71%. Field effect scanning electron microscopy (FESEM) was used, revealing an irregular spherical shape with sizes ranging from 13.829 to 56.233 nm. Energy-dispersive X-ray (EDX) analysis showed the presence of Ag atoms at specific ratios in the sample. Zeta potential analysis confirmed the presence of negative charges on the green-synthesized particles (-18.7 mV). X-ray diffraction (XRD) analysis showed prominent peaks at 38.19, 44.37, 64.56, and 77.47, corresponding to the (111), (200), (220), and (311)

In this study, the volatile compounds found in lemon trees infested and uninfested with Planococcus citri (Risso) (Hemiptera: Pseudococcidae) were investigated. In addition, the interest of the predator Cryptolaemus montrouzieri (Coleoptera: Coccinellidae) and the parasitoid Leptomastix dactylopii (Hymenoptera: Encyrtidae) in lemon trees infested and uninfested with P. citri and some volatile compounds was investigated. According to the results obtained, most of the volatile compounds obtained from mealybug-infested lemon trees showed changes compared to healthy lemon trees. Since volatile compounds play an important role in attracting pests and natural enemies, linalyl acetate was selected as the compound showing the highest amount of chan

Machine learning is considered a powerful technique in many applications such as classification, clustering, recognition and prediction. Deep learning is a modern, vital and superior machine learning that gives stunning performance, especially with huge data. Stock market price prediction is the process of determining the future value of a prospect of a financial instrument traded in the market, to gain a great profit a successful prediction must be conducted, in order to achieve that machine learning is used, in this article, two approaches are proposed to predict the stock market prices and movement using two datasets, the first approach employs two machine learning models (J48 & logistic regression) while the second approach based on rec

Natural settings make it challenging to identify facial expressions since head position, illumination level, and ‎‎occlusion vary. Thus, developing a more generic model without front-facing images alone is quite crucial. This ‎research proposes a facial expression ‎recognition model based on pre-trained deep convolutional neural networks ‎with transfer learning. The model was trained ‎on several cases to classify face expressions into seven ‎classifications efficiently. The proposed system used the EfficientNetB0 model ‎that has one dense dropout layer. The model first rescales and norms the input dataset in the input ‎layer that takes images of a larger resolution to get better results. After entering 7 blocks sequential

The increasing demand for continual learning in sequential data processing has led to progressively complex training methodologies and larger recurrent network architectures. Consequently, this has widened the knowledge gap between continual learning with recurrent neural networks (RNNs) and their ability to operate on devices with limited memory and compute. To address this challenge, we investigate the effectiveness of simplifying RNN architectures, particularly gated recurrent unit (GRU), and its impact on both single-task and multitask sequential learning. We propose a new variant of GRU, namely the minion recurrent unit (MiRU). MiRU replaces conventional gating mechanisms with scaling coefficients to regulate dynamic updates of hidden

Continual learning on edge platforms remains challenging because recurrent networks depend on energy-intensive training procedures and frequent data movement that are impractical for embedded deployments. This work introduces M2RU, a mixed-signal architecture that implements the minion recurrent unit for efficient temporal processing with on-chip continual learning. The architecture integrates weighted-bit streaming, which enables multi-bit digital inputs to be processed in crossbars without high-resolution conversion, and an experience replay mechanism that stabilizes learning under domain shifts. M2RU achieves ∼13 GOPS at 16.76 mW, corresponding to 776 GOPS per watt, and maintains accuracy within 5 percent of software baselines on seque

Modern machine-learning applications require GPUs, and modern platforms can leverage numerous GPUs on one or more machines to increase performance. Contemporary deep-learning models are too huge for CPU or GPU training. Training these models with many GPUs without performance degradation is necessary to train them rapidly and maximize GPU consumption. Thus, training deep convolutional neural networks (DCNN) with multiple GPUs has become necessary for improving training. Therefore, we presented a parallel design and development of an efficient model for enhancing face mask CNN performance and improving resource efficiency. This DCNN model is a parallel training system over multiple GPUs, a multi-core CPU, and a multi-process GPU platform wit

Several parameters affect the properties of Portland cement and one of these parameters is the cooling rate of the clinker. If the effectiveness of the cooling method of the clinker increases, a good enhancement in the properties of Portland cement will be found. Depending on the new cooling method suggestion by Nasr et. al. [20], the counter pattern of air clinker flow was studied using (FLUENT 6.3.26). The dimensions of the cooling room in grate cooler, the constant mass flow rate of both clinker and air, different height ratios, and different clinker porosity were considered in this numerical work. The results show that the heat transfers in the first half of the cooling room (0 < X < 0.9 m) is larger than that in the secon

Corrosion resistance of metals is one of the most important topics for researchers. In this work, epoxy-TiO2 nanocomposite has been developed for protection of 6061AL alloy. The dip-coating technique used to deposit the coating on 6061 Al alloy substrate. Different concentrations of TiO2 NPs (1,3,5 )Wt% have been used to study the corrosion behavior in 3.5% NaCl medium. Also, 200,150 and 100 mm/min dipping speeds have been employed for 1%wt TiO2 to show their effects on coating layer and corrosion rate. Characterization of coated surfaces were investigated by AFM, FESEM andEDS,while theelectrochemical measurementswas using to evaluate protective nature of the coating. The results have shown that the corrosion rate has decreased with

ZnTe possesses the proper optoelectronic properties as a candidate for device development. The structure and optical properties of ZnTe semiconductor thin films of 500 nm were studied using thermal evaporation technique. The influence of annealing temperatures on ZnTe thin films in the range ( R.T - 473 K). XRD and surface morphological analyses are used to examine the films. The ZnTe films are comparatively polycrystalline and cubic in phase, according to the XRD analysis. with a lattice constant of 0.61 nm upon an (111) orientation. The intensities of all the peaks rapidly increase though they show the same tendencies; it shows the crystallinity of the films becomes higher crystal size diameters (from 8.41 to 12.18nm) both increas