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Introduction: Caudal block since its first description in 1933 for paediatric urological interventions has evolved to become the most popular regional anesthetic technique for use in children. It provides excellent analgesia during surgery as well as during postoperative period in subumblical surgeries in children. This randomised prospective controlled study was undertaken to find and compare quality and duration of analgesia, motor and sensory block after a single shot caudal block with either Bupivacaine or Ropivacaine.Method: 60 ASA I-II patients in the age range-12 years scheduled for Elective subluminal procedures were enrolled. Standard anaesthetic induction was conducted in all patients. After induction caudal block was performed in the lateral position. Perioperative haemodynamic parameters were recorded. Patients were randomly allocated to one of the two groups of 30 patients each. Group A received 1ml/kg of 0.25% bupivacaine. Group B received 1ml/kg of 0.2% ropivacaine.Results: Post operative pain score was comparable in two groups in the first 4 hours but it was significantly less in ropivacaine group after 4 hour. The mean duration of analgesia in group A was 7.4±1.0 hours while in group B mean duration of analgesia was 7.6±1.3 hours. 19 patients (63.3%) in group A received rescue analgesic as compared to 10 (33.3%) patients in group B during 12 hour study period. Conclusion: Caudal ropivacaine provides effective post-operative analgesia and possessing less motor blockade makes it a suitable agent for day care surgery with increased margin of safety particularly in younger children

In healthcare settings, particularly in areas such as operating rooms and intensive care units, there is a need for a dynamically controlled temperature environment that can adapt to the changing needs of both patients and healthcare workers. This is due to the fact that the desired temperature can vary depending on the condition of the patient and the specific requirements of surgical and treatment procedures. To address this need, our objective is to develop a tool for predicting the electric power needed to maintain a desired temperature in these critical care areas. Previous research has employed artificial learning algorithms and mathematical equations to predict electric power for various types and sizes of buildings, with promising results. However, our study focuses specifically on critical care areas within hospitals and utilizes fluctuating temperature set-points to predict power demand using historical weather data and Building Management System (BMS) data. We employed both Multi-Layer Artificial Neural Network (ML-ANN) and Long short-term memory (LSTM) models for this purpose and found that ML-ANN outperformed LSTM. The results showed that the ML-ANN model performed better than the LSTM model, with a testing accuracy of 96% compared to 78% for the LSTM model. This indicates that the ML-ANN model was more accurate in predicting the power consumption for the desired temperature in the operating room.

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