Notwithstanding the importance of international cooperation as the other facet of international interactions, a strategy of conflict resolution, a maintainer of international peace and security, its provision in the United Nations conventions, as an objective of the United Nations after the international peace and security, however, the recognition of international cooperation has not been underlined by global, intellectual think tanks. While realism emphasized on the state's role in achieving international cooperation to ensure mutual and multilateral interests, liberalism focused on the role of international organizations in building such cooperation. Additionally, constructivist approaches developed other sub-variables to contribute to t

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

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

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

<p>Energy and memory limitations are considerable constraints of sensor nodes in wireless sensor networks (WSNs). The limited energy supplied to network nodes causes WSNs to face crucial functional limitations. Therefore, the problem of limited energy resource on sensor nodes can only be addressed by using them efficiently. In this research work, an energy-balancing routing scheme for in-network data aggregation is presented. This scheme is referred to as Energy-aware and load-Balancing Routing scheme for Data Aggregation (hereinafter referred to as EBR-DA). The EBRDA aims to provide an energy efficient multiple-hop routing to the destination on the basis of the quality of the links between the source and destination. In

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 this work, the role of s ubs trate temperature in the range (293-423)K on the structure and microstructure of CuInSTe thin films was investigated. The results showed that the structure was amorphous for films prepared at ambient temperature Ts=293Kwhile it was polycrys talline for films prepared at high temperature. The grain s ize and roughness increased with increasing substrate temperature. The frequency and temperature dependencies upon AC conductivity were investigated in the frequency range 100Hz-10MHz and temperature of heat treatment in the range of 303-453K. The AC activation energy was found to increase from 0.1132 to 0.1258 eV with increas ing s ubs trate temperature from 293 to 423K and decreas e to 0.0962 eV with inc

Platinum (Pt) supported on sulfated zirconia (SZ) and HY-zeolite as a solid acid catalyst was synthesized successfully for isomerization reaction using precipitation and impregnation method. The physicochemical properties of the catalyst were characterized using various techniques including X-ray diffraction (XRD), Fourier transformation infra-red spectroscopy (FTIR), BET Surface area and pore volume, and Field Emission Scanning Electron Microscopy (FESEM).  The prepared composite catalyst Pt/SZ-HY consisted of high Bronsted acidic sites and Lewis acidic sites. The addition of multi-walled carbon nanotubes (MWCNTs) to SZ increased the surface area and pore volume, resulting in smaller crystal sizes and a narrower particle size dist