Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning.

The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant. It is difficult to predict COD in a real time mode, so it is better to use Complex Mathematical Modeling (CMM) for simulating AD process and forecasting output parameters. Therefore, different Machine Learning algorithms, such as Linear Regression, Decision Tree, Random Forest and Artificial Neural Networks, have been used for predicting effluent SCOD using data acquired from in situ anaerobic wastewater treatment system. The result of the predicted data using different algorithms were compared with experimental data of anaerobic system. It was observed that the Artificial Neural Networks is the most effective simulation technique that correlated with the experimental data with the mean absolute percentage error of 10.63 and R2 score of 0.96. This research proposes an efficient and reliable integrated modeling method for early prediction of the water quality in wastewater treatment.

Mastoid Osteoma of Temporal Bone - A Rare Case Report.

Osteomas of the temporal bone are rare entity with incidence of 0.1% - 1% of all benign tumours of skull. They are slow growing benign lesion of temporal bone. We report a rare case of mastoid osteoma of size more than 5 cm. A 40-year-old female patient presented to us with left post auricular bony swelling of size greater than 5 cm. Diagnosis of mastoid osteoma was made after relevant investigations including High Resolution Computed Tomography (HRCT) temporal bone. Complete surgical excision was done with good cosmetic outcome without recurrence.

Deep Neck Space Infections: A Study of 76 Cases.

INTRODUCTION: Deep neck space infections (DNSI) are serious diseases that involve several spaces in the neck. The common primary sources of DNSI are dental infections, tonsillar and salivary gland infections, malignancies, and foreign bodies. With widespread use of antibiotics, the prevalence of DNSI has been reduced. Common complications of DNSI include airway obstruction, jugular vein thrombosis, and sepsis. Treatment principally comprises airway management, antibiotic therapy, and surgical intervention. This study was conducted to investigate the age and sex distribution of patients, symptoms, presentation, sites involved, bacteriology, and management and complications of DNSI. MATERIALS AND METHODS: This retrospective study was performed from October 2010 to January 2013, and included 76 patients with DNSI. Patients of all age groups and gender were included. All parameters including age, gender, co-morbidities, presentation, site, bacteriology, complications, and required interventions were studied. RESULTS: In our study, the majority of patients were in the 31-50-year age group. Males accounted for 55.26% of the sample and females for 44.74%, with a male:female ratio of 1.23. Most of the patients were from a rural background. Diabetes was found as a co-morbid condition in 10.52% cases. Neck pain was the most common symptom, identified in 89.47% cases. The most common etiological factor was odontogenic infection (34.21%), followed by tonsillar and pharyngeal infection (27.63%). The most common presentation was Ludwig's angina (28.94%), followed by peritonsillar abscess and submandibular abscess. In 50% of cases, Streptococcus and Staphylococcus were found in the culture. Surgical intervention was carried out in 89.47% cases. Emergency tracheotomy was required in 5.26% cases. CONCLUSION: DNSI can be life-threatening in diabetic patients, the immunocompromised, and elderly patients, and special attention should therefore be given to these groups. Early diagnosis and treatment is essential to prevent complications. All patients must be treated initially with intravenous antibiotics, with treatment subsequently updated based on a culture and sensitivity report. Due to poor oral hygiene, lack of nutrition, smoking and chewing of beetle nut and tobacco, odontogenic infections are the most common cause of DNSI. Thus, DNSI could be prevented by making the population aware of dental and oral hygiene and offering regular check-ups for dental infections.