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.

Flaps for Oral and Maxillofacial Reconstruction: Review of Literature and a Clinical Guide to the Clinicians

There are many different ways documented for reconstruction of oral and maxillofacial (OMF) region among which the use of flaps (local or distant) is commonly practiced worldwide. Modern techniques of OMF reconstruction aim to restore function as well as improve aesthetics. This article intends to review the literature on varied flaps used in oral and maxillofacial surgery (OMFS) and summarize their precise clinical implications taking into consideration the ease, difficulties, and the ultimate clinical outcomes. A review of the literature of local or distant flaps used in OMFS was done using Google database. There are many different methods of reconstruction in patients who have had defect in the OMF region due to vascular reasons. It is important for the OMF surgeon who is involved with the management of such patients to have an understanding regarding the choice of flap used to reconstruct such defects.

Recurrent Neural Network based Soft Sensor for Monitoring and Controlling a Reactive Distillation Column

For the real time monitoring of a Reactive Distillation Column (RDC), a Recurrent Neural Network (RNN) based soft sensor has been proposed to estimate the bottoms product composition of the RDC for the synthesis of n-Butyl Acetate using esterification reaction. This soft sensor acts as a measuring element in a closed loop involving a PI controller for the direct control of the RDC’s product concentration. The RNN acts as a dynamic network, which works on the sequential input data and output data with a recurrent connection. While using the RNN based soft sensor in the open loop, it has been observed that the sensor estimated the composition of butyl acetate in the bottoms with such an accuracy that it can be used for the control purpose. Closed loop results demonstrated that the system has been showing precise controlled results and soft sensor is showing small prediction Mean Square Error (MSE) when disturbances in feed flow rate and set point changes are introduced.

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.