scholarly journals Evaluasi Stabilitas Sifat Fisika Kimia Sediaan Krim Ketoconazole dengan Metode Stabilitas Penyimpanan Jangka Panjang

2021 ◽  
Vol 6 ◽  
pp. 162
Sudrajat Sugiharta ◽  
Widia Ningsih

Mutu sediaan obat penting dipertahankan selama proses pembuatan, penyimpanan hingga digunakan. Stabilitas sediaan krim merupakan salah satu kriteria yang penting dari mutu sediaan tersebut karena akan berdampak pada efektifitas, keamanan dan mutu produk pada saat digunakan oleh masyarakat. Penelitian ini bertujuan untuk menguji stabilitas sifat fisika sediaan krim ketoconazole dengan metode stabilitas penyimpanan jangka panjang (real time). Penelitian menggunakan praeksperimental dengan rancangan one shot case study dengan pengujian stabilitas jangka panjang sediaan krim ketoconazole selama 42 bulan dengan kondisi penyimpanan 30⁰C ± 2⁰C RH 75% ± 5%. Organoleptik, pH, viskositas, kadar zat aktif ketoconazole diperiksa dengan interval waktu penyimpanan selama 24, 30, 36, dan 42 bulan. Penelitian menunjukkan bahwa efek penyimpanan selama 42 bulan pada pengujian organoleptik tidak menyebabkan perubahan bau, warna, dan tekstur, dimana tidak terbentuk lapisan krim serta masih mempertahankan kehomogenan. Pada pengujian pH, dan viskositas dari krim ketoconazole tidak berubah secara signifikan (p ≥ 0.05) setelah 42 bulan penyimpanan pada kondisi penyimpanan 30⁰C ± 2⁰C RH 75% ± 5%. Pada penetapan kadar zat aktif ketoconazole dalam krim selama penyimpanan tidak menunjukan degradasi yang signifikan (terjadi penurunan kadar kurang dari 5% pada penetapan kadar). Data yang diperoleh dari uji stabilitas jangka panjang menunjukan bahwa sediaan krim ketoconazole tetap stabil selama 42 bulan penyimpanan.

1997 ◽  
Vol 36 (8-9) ◽  
pp. 331-336 ◽  
Gabriela Weinreich ◽  
Wolfgang Schilling ◽  
Ane Birkely ◽  
Tallak Moland

This paper presents results from an application of a newly developed simulation tool for pollution based real time control (PBRTC) of urban drainage systems. The Oslo interceptor tunnel is used as a case study. The paper focuses on the reduction of total phosphorus Ptot and ammonia-nitrogen NH4-N overflow loads into the receiving waters by means of optimized operation of the tunnel system. With PBRTC the total reduction of the Ptot load is 48% and of the NH4-N load 51%. Compared to the volume based RTC scenario the reductions are 11% and 15%, respectively. These further reductions could be achieved with a relatively simple extension of the operation strategy.

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

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