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Deep reinforcement learning usage in creating intelligent agents for various tasks has shown outstanding performance, particularly the Q-Learning algorithm. Deep Q-Network (DQN) is a reinforcement learning algorithm that combines the Q-Learning algorithm and deep neural networks as an approximator function. In the single-agent environment, the DQN model successfully surpasses human ability several times over. Still, when there are other agents in the environment, DQN may experience decreased performance. This research evaluated a DQN agent to play in the two-player traditional board game of Surakarta Chess. One of the drawbacks that we found when using DQN in two-player games is its consistency. The agent will experience performance degradation when facing different opponents. This research shows Dueling Deep Q-Network usage with increasing batch size can improve the agent's performance consistency. Our agent trained against a rule-based agent that acts based on the Surakarta Chess positional properties and was then evaluated using different rule-based agents. The best agent used Dueling DQN architecture with increasing batch size that produced a 57% average win rate against ten different agents after training for a short period. ABSTRAK: Pembelajaran Peneguhan Mendalam adalah terbaik apabila digunakan bagi mewujudkan ejen pintar dalam menyelesaikan pelbagai tugasan, terutama jika ia melibatkan algoritma Pembelajaran-Q. Algoritma Rangkaian-Q Mendalam (DQN) adalah Pembelajaran Peneguhan berasaskan gabungan algoritma Pembelajaran-Q dan rangkaian neural sebagai fungsi penghampiran. Melalui persekitaran ejen tunggal, model DQN telah beberapa kali berjaya mengatasi kemampuan manusia. Namun, ketika ejen lain berada dalam persekitaran ini, DQN mungkin kurang berjaya. Kajian ini melibatkan ejen DQN bermain papan tradisional iaitu Catur Surakarta dengan dua pemain. Salah satu kekurangan yang dijumpai adalah konsistensi. Ejen ini akan kurang bagus ketika berhadapan lawan berbeza. Kajian menunjukkan dengan penggunaan Rangkaian-Q Dwipertarungan Mendalam bersama peningkatan saiz kumpulan dapat meningkatkan konsistensi prestasi ejen. Ejen ini telah dilatih untuk melawan ejen lain berasaskan peraturan dan sifat kedudukan Catur Surakarta. Kemudian, ejen ini diuji berpandukan peraturan berbeza. Ejen terbaik adalah yang menggunakan rekaan DQN Dwipertarungan bersama peningkatan saiz kumpulan. Ianya berhasil memenangi permainan dengan purata 57% berbanding sepuluh agen lain melalui latihan jangka masa pendek.

Social networks and communication on theInternet have an important role in people's daily lifeworldwide. This paper examines the impact of Socialnetwork usage on the Academic performance of students. The research data were collected from a stratified sampling survey including 576 Vietnamese undergraduate students. The research model has five independent variables (Perceived usefulness, Perceived ease of use, Social influences, Facilitating conditions, Community identity), one mediating variable (Social network usage), and onedependent variable (Academic performance). The studymethods include the Cronbach’s alpha test, the Exploratory factor analysis, the Confirmatory factor analysis, and the Structural equation modeling are employed toquantitatively analyze the relationship among variables.The result finds that there are positive and significantimpacts of Perceived usefulness, Perceived ease of use,Social influences, Community identity on Social networkusage. The impact of Facilitating conditions on Socialnetwork usage is positive, however, it is not significant.The result also concludes that there is a positive andsignificant impact of Social network usage on theAcademic performance of students. Besides, the empiricalstatistical data indicates that social networks are really apopular and familiar tool for helping students tocommunicate with each other in Vietnam. Finally, thestudy result suggests that educational managers may usesocial networks as an informal tool to enhance not only the academic performance of students but also othereducational activities in universities.

This paper uses graph theoretical measures to analyse the relationship between street network usage, as well as other street- and area-level factors, and dissident Republican violence in Belfast. A multi-level statistical model is used. Specifically, we employ an observation-level random-effects (OLRE) Poisson regression and use variables at the street and area levels. Street- and area-level characteristics simultaneously influence where violent incidents occur. For every 10% change in the betweenness value of a street segment, the segment is expected to experience 1.32 times as many incidents. Police stations (IRR: 22.05), protestant churches (IRR: 6.19) and commercial premises (IRR: 1.44) on each street segment were also all found to significantly increase the expected number of attacks. At the small-area level, for every 10% change in the number of Catholic residents, the number of incidents is expected to be 4.45 times as many. The results indicate that along with other factors, the street network plays a role in shaping terrorist target selection. Streets that are more connected and more likely to be traversed will experience more incidents than those that are not. This has important practical implications for the policing of political violence in Northern Ireland generally and for shaping specific targeted interventions.

Background: According to the spread and popularity of social networks and easy access to them through smartphones, the impact of these networks’ long-term application and consequences, including psychological vulnerability, could not be ignored. Objectives: The present study investigated the mediating role of using smartphone social networks considering personality traits and psychological vulnerability. Methods: The research population included all users (over 18 years of age) of social networks in Isfahan, Iran, from which 250 individuals (i.e. 96 male and 154 female subjects) were selected. The research method was descriptive and causal-correlational. For data collection, the short-form five-factor personality questionnaire neuroticism-extraversion-openness (NEO), the smartphone social media addiction questionnaire, and a psychological vulnerability questionnaire were used. The Pearson correlation coefficient and the path analysis of structural equation modeling were employed to analyze the data. Results: The findings revealed that among personality factors, neuroticism and low extraversion could explain the use of social networks in a significant and desirable way. Moreover, using social networks could explain psychological vulnerability in the form of causal-structural relationships. Conclusions: The findings indicated a relatively good fit of the measurement model. Accordingly, the variable of social network usage plays a mediating role in the relationship between psychological vulnerability and some personality traits.

The application mode of army mobile communication networks is closely related to combat mission and application environment. Different combat missions and application environments result in different network structures and different service priorities, which requires a semi-automatic system to support the network scheme design. Therefore, evaluating the efficiency of network schemes generated by automatic planning is a problem that needs to be urgently addressed. In the past, researchers have proposed a variety of methods to evaluate the effectiveness of mobile communication systems, most of which are based on simulation methods and ignore the historical data of network usage. This paper studies an effectiveness evaluation method of mobile communication network design schemes and proposes a design scheme for the evaluation and optimization of network plans. Furthermore, the improved method of effectiveness evaluation based on factor analysis is discussed in detail. The method not only effectively uses historical data but also reduces the amount of data collection and calculation. In order to adapt to the preference requirements of different task scenarios, a decision preference setting method based on cluster analysis is proposed, which can render the output optimization result more reasonable and feasible.

The object of research is an approach of functionality extension for simulation toolkits based on iFogSim. It is assumed by the native approach that enhancement of functionalities should be achieved by inheriting the fog device class and defining new features in its body. However, this approach makes it impossible to use inherited simulators together and significantly decreases flexibility even when utilizing a single simulator. Another problem related exclusively to iFogSim is a specific communication scheme between application modules, which results in data routing limitations in fog architectures and odd data streams taken into account. This paper introduces an alternative extension approach incorporating a peculiar inheritance scheme which tries to reconsider the standard approach from a behavioral design patterns point of view. The key feature of the suggested approach is an extraction of fog device features from the native class into separate behavioral classes. Meanwhile, the designed inheritance scheme allows to flexibly override and combine behaviors. According to the approach principles the developed simulator extends iFogSim with application modules addressing capabilities solving limitations, along with implementing users’ mobility and dynamic wireless connectivity as it is done in MobFogSim. With the aim to check its correctness, the designed toolkit was validated with the standard for iFogSim case study of «EEG Tractor Beam game» application. The validation included four scenarios. In the first two scenarios the features of users’ mobility and dynamic base station connectivity were validated. And in the next scenarios that utilized address routing the obtained delay and network usage values were compared with theoretically calculated ones. The validation results indicated the correct simulator behavior, and introduced functionalities extension approach, being more complex in comparison with the inative one, can significantly improve flexibility of the simulator

The increasing penetration level of distributed energy resources (DERs) increases the risk of congestion in the distribution network. To mitigate this, the concept of the small-scale DER aggregator was introduced as a change from uncoordinated to coordinated DERs. However, without appropriate network use cost allocation, the unwanted DER curtailment will be enforced by the network operator. Therefore, this paper proposes a new approach for congestion management by allocating the different network usage costs depending on how much congestion is caused by the DERs in the distribution network. For this, a modified Kirschen’s tracing method is proposed and applied to the small-scale DER aggregator market. To verify the effectiveness of the proposed method, a simulation of the small-scale DER aggregator market in South Korea was performed under the IEEE 69-bus distribution network. The model was able to allocate the different network usage costs at different buses and, thus, encouraged the DERs to reduce their generation by charging the energy storage system (ESS) to mitigate congestion. An economic benefit analysis was also performed from the point of view of the aggregator concerning whether they should have an ESS or not.