A real MSW conversion into bio-oil by pyrolysis method had been investigated. The pyrolysis experiment was carried out in an isothermal fixed bed reactor at 400 °C for 60 min. Natural activated zeolite and kaolin catalysts were employed in this process with catalyst-MSW at ratio of 0.5 w/w. Influence of activated catalyst to pyrolysis productivity and bio-oil properties were studied. Comparison between zeolite and kaolin catalytic pyrolysis was examined and bio-oil products were analyzed by GC-MS. The yields of liquid fuel or bio-oil were found around 23.6 and 21.4% for kaolin and zeolite catalytic pyrolysis, respectively. The carbon range distribution in bio-oil were 65.38% of gasoline range (C₅-C₁₂) and 33.49% of diesel range (C₁₃-C₂₀) for activated kaolin catalytic cracking. Activated zeolite had 51.62% of gasoline range (C₅-C₁₂) and 48.37% of diesel range (C₁₃-C₂₀). The results showed that both of bio-oil products mostly contained paraffin and olefin. The use of kaolin as catalyst was better than zeolite in converting the real MSW into bio-oil.

The chest X-ray (CXR) is a commonly used diagnostic imaging test that requires significant expertise and careful observation due to the complex nature of the pathology and fine texture of lung lesions. Despite the long-term clinical training and professional guidance provided to radiologists, there is still the possibility of errors in diagnosis. Therefore, we have developed a novel approach using a convolutional neural network (CNN) model to detect the abnormalities of CXR images. The model was optimized using algorithms such as Adam and RMSprop. Also, several hyperparameters were optimized, including the pooling layer, convolutional layer, dropout layer, target size, and epochs. Hyperparameter optimization aims to improve the model’s accuracy by testing various combinations of hyperparameter values and optimization algorithms. To evaluate the model’s performance, we used scenario modeling to create 32 models and tested them using a confusion matrix. The results indicated that the best accuracy achieved by the model was 97.94%. This accuracy was based on training and test data using 4538 CXR images. The findings suggest that hyperparameter optimization can improve the CNN model’s accuracy in accurately identifying CXR abnormalities. Therefore, this study has important implications for improving the accuracy and reliability of CXR image interpretation, which could ultimately benefit patients by improving the detection and treatment of lung diseases. Acknowledging dataset constraints, we address future steps for model improvement.

Purpose of the study: The purpose of this study is to empirically examine the factors that influence traditional fishermen in maintaining the resilience of marine areas in order to defend the country and examine the model of causality relationships that are formed as well as the influence of causality between these variables. Methodology: The study was conducted in the Depok coastal area, Bantul - Yogyakarta with traditional fishermen as the respondents who are members of the Mina Bahari Fishermen Cooperative. Hypothesis testing was based on Partial Least Square. Main Findings: This study found: (1) 15 reflexive indicator statements form 7 latent variables that directly to construct a hypothesis; (2) a state defence model is formed; (3) hypothesis testing model shows that the seven proposed hypotheses are accepted, namely: a) Knowledge of environmental security has a positive influence on attitudes towards environmental security; b) Attitudes towards environmental security and environmental influences have a negative influence on actions against security disturbances; c) Understanding the environment has a positive influence on environmental awareness; d) Environmental awareness, environmental influences, and actions against security disturbances have a positive influence on participation in the environment. Applications of this study: This study helps inform the stakeholders establish coastal resilience program by incorporating the fishermen in the coastal areas. Novelty: Few studies paid attention toward traditional fishermen to keep marine resilience in Indonesia.

The Depok Village, Indonesia, has a potential of marines and fish resources up to reach 11, 374.1 tons in 2015 to 2017. However, there is a strong evidence that the climate change (i.e. sea temperature, rain fall and humidity) has affected the distribution of fish production and has implications for the marine sector of capture fishery production. This study is to investigate the relationships between the climate changes and fish production in Depok village. Data on the climate change and the annual yield of fish’s production during the period 2016 to 2017 were analysed. The result shows that there was a significant correlation between the climate change and annual fish yield or catch with r = 0.64 and p=0.013. The study also reveals a decreasing production from 4028 kg in 2016 to 3600 kg in 2017 of fish caught in just over one year period. This study contributes recommendations for Government agencies by providing data and information related to temperature and climate changes for fishermen. In brief, government agencies need to develop information systems for monitoring the alarming sea conditions.

One of the most recent studies on the analysis of complex systems is to understand the role of community structure and centrality in analyzing the networks of complex systems such as protein and social networks. Traditional measures of centrality – degree centrality, closeness centrality, and betweenness centrality – cannot capture how community structures within these networks configure them. In this regard, we propose a new community-consideration centrality method to fill this gap. This method includes a weight of consideration, α, ranging from 0.0 to 1.0, to balance the focus between community and network-wide importance in the centrality calculations. Our analysis of two zachary karate and dolphin datasets shows that including community consideration in the degree, closeness, and betweenness centrality measures accurately captures the proportional significance of both communities and networks. In particular, for the lung adenocarcinoma cancer protein case study, our method not only identified more cancer hallmark genes than the traditional centrality measures without considering communities but also outperformed several other advanced centrality algorithms regarding the detection of crucial cancer-related genes. A balanced objective between network and community impacts was observed at an optimum performance α values of 0.1 and 0.2. It finds a strong significance of community structure in network analysis and features a more nuanced perspective on centrality in complex systems.

Kitabisa atau kitabisa.com adalah platform untuk menggalang dana dan berdonasi secara online terpopuler di Indonesia. Kitabisa.com menyediakan frequently ask questions (FAQ) untuk membantu visitor mengetahui mengenai Kitabisa.com dan bagaimana menggunakan layanan yang Kitabisa.com sediakan. Pada penelitian ini akan mengusulkan satu solusi untuk memaksimalkan otomatisasi layanan FAQ Kitabisa.com berbasis Natural Language Processing (NLP). Layanan ini dilakukan dengan fitur bot pada messenger Telegram yang dirancang berbasis NLP menggunakan teknologi TensorFlow. Dengan demikian, chatbot ini bertindak sebagai customer service yang akan menjawab pertanyaan-pertanyaan yang diajukan. Hasil dari penelitian ini adalah chatbot dengan menggunakan metode NLP dapat memberikan respon sesuai dengan konteks atas apa yang user tanyakan dengan akurasi 73%.