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Introduction: Idiopathic spontaneous spinal subdural hematoma (ISSSDH) is uncommon in occurrence, and its association with concomitant intracranial subdural hematoma (ISDH) is very exceptional. Lack of recognition of ISDH in a patient with SSDH can lead to unanticipated events. We report a rare case of ISSSDH and unrecognized ISDH and treated surgically with a good outcome. Case Report: A 71-year-old gentleman presented with features of spinal neurogenic claudication of 2 weeks duration and was diagnosed to have ISSSDH of the lumbar spine based on magnetic resonance imaging (MRI). In view of an impending cauda equina syndrome, he underwent an emergency decompression through a laminectomy, durotomy, and clot evacuation from L2 to L5. The next day, he developed sudden-onset hemiparesis and altered sensorium. The computed tomography (CT) scan of the brain demonstrated an ISDH, for which emergency burr hole evacuation was done. The patient improved rapidly after the surgery and regained his normal power, sensorium, and achieved comfortable ambulation within a week. Follow-up CT of the brain and MRI scan of the spine revealed adequate decompression. Since the CT features of ISDH were of acute on chronic nature, we presume that it had existed before the onset of spinal symptoms. Conclusion: Through this very rare case, we highlight the importance of screening the brain pre-operatively in patients who present with sub-acute spontaneous spinal SDH.

The first IIMs were established by a coalition of partners with diverging ideas and perspectives on the role of management education in developing India. For some, the management schools materialized the ambition to create a Nehruvian India, based upon socialist principles, while for others they formed a project to foster liberal values and norms in the ‘non-western’ world. This chapter uses the concept of ‘boundary object’, developed by Geoffrey Bowker and Susan Leigh Star in the context of science and technology studies, to explore these diverging interpretations of the management school and to describe how these were reconciled in difficult negotiations and coalition building process that preceded the establishment of the first IIMs from 1955 to 1961.

n-Octanol (C8H17OH) is an advanced biofuel derived from ligno-cellulosic biomass that is suitable for compression ignition technology with several properties closer to fossil diesel. This study analyses the performance and emissions of a direct-injection (DI) diesel engine fueled with n-octanol/diesel blends containing 10% (OCT10), 20% (OCT20) and 30%(OCT30) by volume of n-octanol using a 3 × 3 full-factorial experimental design matrix that considers blend composition of n-octanol in diesel, exhaust gas recirculation (EGR) rates of 10%, 15% and 20% and injection timings of 19°, 21° and 23° crank angle (CA) before top dead centre (bTDC) as factors. Models for oxides of nitrogen (NOx), smoke, brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) were developed using response surface methodology (RSM) and were found to be significant statistically. The variation of EGR had a considerable effect on both BTE and BSFC of the engine followed by blend composition and injection timing. Best performance (BTE = 37.06%, BSFC = 0.23kg/kWh) was delivered by OCT10 at 10% EGR and 23°CA while the lowest performance (BTE = 30.95%, BSFC = 0.28kg/kWh) was by OCT30 at 20% EGR and 19°CA. Injection timing was found to have the highest effect on NOx emissions while EGR affected smoke opacity to the maximum. NOx was found to decrease from 1790 ppm (for OCT10 at 10% EGR and 23°CA) to as low as 410 ppm (for OCT30 at 20% EGR and 19°CA). Smoke opacity was found to decrease from 94.2% (for OCT10 at 20% EGR and 19°CA) to as low as 43% (for OCT30 at 10% EGR and 23°CA). Desirability approach was used to determine the best combination of blend composition of n-octanol, EGR and injection timing for minimising smoke, NOx and BSFC simultaneously. 17% by volume of n-octanol/diesel blend injected at 20° CA bTDC and 10% EGR was predicted to be optimum which delivered a simultaneous reduction of NOx (−47.4%), smoke (−21.08%) and BSFC (−8%) during confirmatory tests with a reasonable accuracy of within 4%. This method is robust and could be employed to other small engines for developing models that can predict engine characteristics with reasonable accuracy.

Urban planning and development is a decisive factor that increases the automobile numbers which leads to increased energy demand across the globe. In order to meet the escalating requirements of energy, it is necessary to find viable alternatives. Waste plastic oil (WPO) is one such alternative which has dual benefits as it reduces the environmental pollution caused by plastic waste and it could possibly meet the energy requirement along with fossil fuels. The study attempted to reduce emissions from a DI diesel engine fueled with WPO using 30% by volume of n-pentanol with fossil diesel (WPO70P30). EGR (10, 20, and 30%) and injection timing modifications were made with the intention to find optimum engine operating conditions. The experimental results indicated that addition of renewable component like n-pentanol had improved the combustion characteristics by igniting WPO more homogeneously producing a higher premixed combustion phase. Smoke density for WPO70P30 was found to be twice lower than that of neat WPO at standard injection timing of 23°CA bTDC at any given EGR rate, NOx emissions were slightly on the higher side about 12% for WPO70P30 blend against WPO at same operating conditions. WPO70P30 showed lowest smoke and carbon monoxide emissions than diesel and WPO while delivering BTE’s higher than WPO and closer to diesel at all EGR and injection timings. However NOx and HC emissions increased with n-pentanol addition. The use of EGR reduced NOx emissions but was found to aggravate other emissions. It was concluded WPO70P30 can be favorably used in a DI diesel engine at the engines advanced injection timing for better performance than diesel with a slight penalty in NOx emissions.

Android being a widely used mobile platform has witnessed an increase in the number of malicious samples on its market place. The availability of multiple sources for downloading applications has also contributed to users falling prey to malicious applications. Classification of an Android application as malicious or benign remains a challenge as malicious applications maneuver to pose themselves as benign. This paper presents an approach which extracts various features from Android Application Package file (APK) using static analysis and subsequently classifies using machine learning techniques. The contribution of this work includes deriving, extracting and analyzing crucial features of Android applications that aid in efficient classification. The analysis is carried out using various machine learning algorithms with both weighted and non-weighted approaches. It was observed that weighted approach depicts higher detection rates using fewer features. Random Forest algorithm exhibited high detection rate and shows the least false positive rate.