Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we

In many applications such as production, planning, the decision maker is important in optimizing an objective function that has fuzzy ratio two functions which can be handed using fuzzy fractional programming problem technique. A special class of optimization technique named fuzzy fractional programming problem is considered in this work when the coefficients of objective function are fuzzy. New ranking function is proposed and used to convert the data of the fuzzy fractional programming problem from fuzzy number to crisp number so that the shortcoming when treating the original fuzzy problem can be avoided. Here a novel ranking function approach of ordinary fuzzy numbers is adopted for ranking of triangular fuzzy numbers with simpler an

The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com

An environmentally begnin second derivative spectrometric approach was developed for the estimation of the dissociation constants pKa(s) of metformin, a common anti-diabetic drug.  The ultraviolet spectra of the aqueous solution of metformin were measured at different acidities, then the second derivative of each spectrum was graphed. The overlaid second derivative graphs exhibited two isobestic points at 225.5 nm and 244 nm pointing out to the presence of two dissociation constants for metformin pKa1 and pKa2, respectively. The method was validated by evaluating the reproducibility of the acquired results by comparing the estimated values of the dissociation constants of two different strategies that show excellent matching. As we

Forty one isolates of genus Proteus were collected from 140 clinical specimens such as urine, stool, wound, burn, and ear swabs from patients of both sex. These isolates were identified to three Proteus spp. P. mirabilis, P. vulgaris and P. penneri .The ability of these bacteria to produce L-asparaginase II by using semi quantitative and quantitative methods was determined. P. vulgaris Pv.U.92 was distinguished for high level of L-asparaginase II production with specific activity 1.97 U/mg. Optimum conditions for enzyme production were determined; D medium with 0.3% of L-asparagine at pH 7.5 with temperature degree 35°C for incubation. Ultrasonication was used to destroy the P. vulgaris Pv.U.92 cells then ASNase II was extracted and pu

Our goal in this research, some new nucleoside analogues was synthesized. Starting from ?-D glucose which was converted to per acetylated ?-D gluco pyronoside then converted to active from(1-Bromo Sugar (2) as a sugar moiety.The base moiety 2-substituted benzimidazole was prepared from condensation of phenylene diamine with different aromatic aldehydes, which were subjected to amino alkylation via Mannich reaction forming new nucleobase derivatives. Condensation of nucleobase with bromo sugar through nucleophilic substitution of anomeric carbon with nitrogen forming new protected nucleoside analogues then hydrolyzed with sodium methoxide in methanol to obtain our target, the free nucleoside analogues. All prepared compound were identified b

The study included the extraction of volatile oil from Mentha piperita which was 1.3 % in the leaves and flowers . Volatile oil of the Mentha piperita leaves had special aromatic odour, pale yellow color, slightly pungent taste . The specific gravity and refractive index were (0.9794) and ( 1.464) respectively. The inhibition activity of the Mentha piperita Volatile oil extracts were studied on some pathogenic microorganisms like Staphylococcus aureus, Salmonella typhi, Escherichia coli, Proteus sp, and Klebsiella pneumoniae . The result showed that the volatile oil had an inhibition effect on the growth of all microorganisms, and it gave the higher inhibition effect on the growth of S. aureus in which the inhibition zone reached to 2