Sarcasm is often used to convey the opposite of what is actually said (positive words, negative meaning). It is a type of humor that relies on the listener or reader to understand the intended meaning of the words. Sarcasm can be difficult to detect in written text, especially on social media platforms where people often use abbreviated language and emojis to convey their messages. The sarcasm detection can be a crucial role in refining Automated Sentiment (SA). In order to enhance the effectiveness of data-driven approaches for customer service and product development. Furthermore, the existing research on sarcasm detection in Arabic is limited. Also, there is a noticeable absence of studies in particular, the sarcasm detection in Libyan Arabic dialects. This study utilizes a data-set of 5082 collected from Facebook platform, labeled by team annotators experienced in the Libyan dialects as (sarcastic or non-sarcastic), and extracting some lexical and syntactic features which used to training Support Vector Machine (SVM) model. The results show commendable performance with an accuracy of 79.15%, precision of 79.3%, recall of 79.7%, and an F1 score of 79.5%.

Net Zero Energy (NZE) buildings play a crucial role in meeting the Sustainable Development Goals (SDG) and creating environmentally friendly residential areas. These buildings are designed to generate as much energy as they consume, resulting in a net balance of zero energy consumption from the grid. By integrating innovative technologies and sustainable design principles, NZE buildings minimize their carbon footprint and contribute to a more sustainable future. The acquired result has been presented and discussed. The concept of Net Zero Energy Buildings (NZEBs) has gained significant attention in recent years as a crucial strategy for achieving sustainability in the built environment. NZEBs are designed to produce as much energy as they consume, resulting in a net energy balance of zero over a specified period.

Abstract—Fault detection and classification is critical to the reliability of modern control systems in different industries, where detecting and classifying faults in operational processes are very important things while failure to detect and classify them, may cause irreparable damage. In this paper, fault detection and classification approach is presented. The first step, multi stage recursive least squares parameter estimation approach for controlled autoregressive autoregressive moving average systems (CARARMA) is developed with a view to estimate the parameters of the system, additionally, improve the effectiveness of the computation. By means of multi stage approach, the (CARARMA) system is decomposed into three simple identification models, and the parameters of each simple model is identified one by one. These parameters estimated by this approach are referred to as features, and not all of them have the same useful data about the system. In order to select the valuable features and improve a classification accuracy, the Linear Discriminant Analysis (LDA) approach based on scattering matrices is applied for dimension reduction. The classification between these reduced classes is done based on the Naive Bayes classifier. Finally, the obtained results explain the performance of this proposed approach.

The aim of this study was to partially determine the nutritional and functional properties of cookies enriched with green lentil dietary flour proposed for children. The proposed cookies were enriched with lentil dietary flour at different concentrations, at the expense of the proportion of wheat flour. The cookies were divided into three different groups based on different formulas, the first group was without green lentil (control), while the other two groups were enriched cookies with green lentil flour “6%” and “12%”.

Results of protein analysis showed that cookies with 6% lentil flour had the highest protein content. Results of protein solubility indicated that cookies fortified with green lentil flour had the highest extraction values in comparison to the control samples. The molecular weight of proteins from ingredients (lentils, wheat, skimmed milk, whey protein, and egg), extracted by high ionic strength buffers, remained unchanged. The combination of ingredients normally impacts a change in proteins, but the major proteins preserve their nutritional characteristics even after being mixed in different formulas. The results showed that the dry matter and ash contents of cookie samples increased by the addition of lentil dietary flour. Colour measurement results showed that the control samples had the highest L* values, while samples of resulted in acceptable quality characteristics in both cookie types (6 and 12%). Overall, data indicates cookies enriched with green lentil dietary flour have fortified their protein and phytochemical contents. The utilization of protein powder derived from lentils may enhance the nutritional quality and functional properties of cookies that can be proposed for school and pre-school kids.

Abdulatef Mrghni Ahhmed Nesr, Şule Daştan2, Mustafa Çam, Naema Saad Gumma Shibani, Mustafa Tahsin Yilmaz, Shiro Takeda, Ryoichi Sakata, (05-2024), انجلترا: ECronicon open access, 19 (5), 1-17

Abstract: Network operators are confronted with the demanding requirements resulting from the evolution of IP networks. As a result, it has become necessary to provide rigorous Service Level Agreements (SLAs) that are in line with these requirements. However, traditional IP networks lack the necessary flexibility, scalability, and manageability to meet these demands. In order to address these limitations, the segment routing (SR) architecture has been developed. SR is based on source-routing and tunneling paradigms, which enable IP/MPLS and IPV6 networks to operate in a simplified and more scalable manner. The focus of this paper is on network protection (resiliency) using Topology Independent Loop-Free Alternatives Fast Re-Route (TI-LFA FRR) using MPLS as the underlying technology. SR overcomes the limitations of previous network protection mechanisms in terms of coverage and optimal path selection. To show the effectiveness of SR TI-LFA in comparison to its predecessor, Remote Loop-Free Alternate (rLFA), we have implemented various scenarios. These scenarios are designed to highlight the superior capabilities of TI-LFA. 

The classical Model Predictive Control (MPC) is still considered in many industrial applications, although advanced control methods have seen  significant development over the last few years. However, a lot of MPC strategies still suffer from  robustness to cope with a variety of process dynamics. For the MPC controller to work effectively,  it must be properly tuned. However, MPC is challenging and there is no ideal analytic method to  obtain exact solutions that result in the best desired responses. Based on that, this paper  investigate the robustness performance for a MPC tuning strategy. Three formulae were derived  (Proposed , Proposed  and Proposed ). These formulae are used for calculating the suppression coefficient. The three  formulae were derived by fitting the optimal empirical suppression coefficients for varies process  dynamics commonly found in industry. Simulation results show that the use of the proposed strategy results in good performance compared to other strategies previously proposed. The proposed   strategy also demonstrated a robust performance with respect to modelling errors of process  parameters. The results demonstrate the effectiveness and validity of proposed strategy when  compared with conventional strategies.

This paper investigates the effects of applying fuzzy techniques to artificial neural networks (ANN) for

the classification of biological data. A fuzzy neural networks (FNNs) model was proposed and evaluated

as a system for image classification. This system involved the process of collecting dataset, image

processing and image classification. Patch-based technique is used to present images to the neural

network. Feed-Forward Backpropagation neural networks are used to build the system. Fuzzy Min-Max

Neural Networks (FMNN) approach was used to synthesize Fuzzification and neural networks to generate

fuzzy neural networks that can handle imprecision and uncertainty. The approach is evaluated using

images from the data portal (Papers with Code) website. Experimental results have shown an improvement

in the performance of fuzzy neural networks compared with neural networks.

The quest for reliable water access has been a critical endeavor throughout history, and Rujban City is no exception. Faced with the challenges of aging infrastructure and reliance on external water sources, Rujban is turning towards technological innovation for solutions. This paper outlines the design of the communication component within the Idref Automated Control System 2 (IACS II), a significant advancement in the automation of Rujban's water distribution system. This system innovatively connects the city's central distributed tank to three distributed tanks via a robust wireless communication network, while maintaining a wired connection to the Idref collection tank, the main control station, and associated monitoring rooms and pumps. The system employs microcontroller-based automation to manage water levels, control signals, and monitor pump activity and potential leaks, with data displayed on an LCD screen. This approach is poised to dramatically improve the efficiency and reliability of water distribution in Rujban City, marking a key step forward in addressing its water supply challenges. Key Words: Wireless Network, Transmitter, Receiver, Control System, Water Pumping, Water Distribution, Smart Irrigation System.