Aggressive Fibromatosis: Government Royapettah Hospital Experience

INTRODUCTION: Aggressive Fibromatosis also known as Desmoid tumors is a monoclonal disorder affecting the musculoaponeurotic tissues. It is relatively a rare neoplasm with a frequency of about < 3% of all soft tissue tumors and annual incidence of about 0.2 to 0.5 per 100000 population. Exact etiology of aggressive fibromatosis is currently unknown. It is probably multifactorial with hormonal, genetic and trauma playing their parts. They are locally infiltrative and never metastasize but have a tendency for multiple recurrences. The morbidity caused by this lesion is due to local destruction of tissues and occasional death has been reported. Aggressive Fibromatosis are heterogenous group of tumors which share similar clinical, histological and molecular genetic feature. But each type has some subtle and unique feature which distinguishes it from others. Due to the relative rarity of this tumor and their enigmatic clinical behavior, treatment for fibromatosis has not been optimized. In this article we will be discussing about our experience with deep fibromatosis. AIM OF THE STUDY: 1. To study the epidemiological characteristics of the disease in India. 2. To analyze the surgical data and present the outcome. 3. Describe the pattern of recurrence and salvage modality for recurrence. 4. To find out the optimal management strategy for this rare disease. MATERIALS AND METHODS: A retrospective analysis of our data over a period of 13 yrs (between 1998 and 2010) was done. There were 33 patients with a diagnosis of deep fibromatosis in our records which included abdominal, intra-abdominal and extra abdominal. In 28 patients a wide excision of the lesion was performed with curative intent. Adjuvant radiotherapy was given for 4 patients and systemic therapy in the form of tamoxifen was given for 4 patients. Information regarding epidemiological characteristics of the disease, surgical procedure performed postoperative margin status on histopathological examination, adjuvant treatment given, recurrences in the follow-up period, pattern of recurrence and salvage modality for recurrence were collected for analysis. OBSERVATION AND ANALYSIS: The disease and its natural course to some extent were described 170 years ago. Although numerous researchers have dedicated their time to acquire more knowledge about this disease, every aspect of this disease beginning form etiology to management is full of controversies. The enigma of fibromatosis will continue to haunt the researchers for some more time as new findings (like ERβ) lead to more questions than answering the older ones. In this analysis we will concentrate on the Indian perspective about the disease and explore the lacunae in the literature. CONCLUSION: Demographic trend in India might be different from what is reported in the western literature with more incidences in male sex and pediatric age group. Recurrences after surgery alone can be quite high (40% in our series). Positive margin & recurrent tumors were found to be adverse prognostic factor for recurrence in our series. Even in negative margin the recurrence rate was high this we believe is due to multicentricity of the lesion. Addition of radiation provides good local control but should be used with diligence as most of the recurrences are surgically salvageable and these tumors occur in young patients with long life expectancy. The role of systemic therapy including Tamoxifen needs further evaluation. Aggressive fibromatosis is an enigma occupying the twilight zone between benign and malignant behavior. There are considerable lacunae in our knowledge regarding the natural course of the disease. Due to the rarity and slow growing nature of the disease there had not been many studies with adequate numbers and follow-up to optimize treatment protocol for this tumor. There had never been a randomized control study comparing different treatments undertaken anywhere in the world. Multi institutional prospective randomized control trials may optimize the treatment protocol for this rare and enigmatic disease

Prediction of Stability and Thermal conductivity of MgO Nanofluids using CCRD Statistical Design Analysis

Magnesium oxide nanopowders were synthesized by chemical reduction method in which sodium hydroxide solution was used as a reducing agent. Magnesium nitrate (MgNO3.6H2O) precursor was used for the synthesis of MgO nanopowders. Solid state characterizations of synthesized nanopowders were carried out by infrared spectroscopy (FTIR) and X-ray diffraction (XRD) techniques. Using two step method, synthesized nanopowders were prepared as nanofluids by adding water and ethylene glycol (55:45). Thermal conductivity measurements of prepared nanofluids were studied using transient hot wire apparatus in which maximum thermal conductivity enhancement was observed in nanofluid. CCRD design has been applied to optimize the performance of nanofluid systems. In this regard, the performance was evaluated by measuring the stability and thermal conductivity ratio based on the critical independent variables such as temperature, particle volume fraction and the pH of the solution. A total of 20 experiments were accomplished for the construction of second-order polynomial equations for both target outputs. All the influential factors, their mutual effects and their quadratic terms were statistically validated by analysis of variance (ANOVA). The optimum stability and thermal conductivity of MgO nanofluids with various temperature, volume fraction and particle fraction were studied and compared with experimental reults. The results revealed that, at increase in particle concentration and pH of nanofluids at certain point would increase thermal conductivity and become stable at nominal temperature.  According to the results, the predicted values were in reasonable agreement with the experimental data as more than 95%  of the variation could be predicted by the CCRD model for thermal conductivity ratio and zeta potential

Crop Insurance Premium Recommendation System Using Artificial Intelligence Techniques

Purpose: The objective of this study is to build a crop insurance premium recommender model which will be fair to both crop insurance policy holders and crop insurance service providers.   Theoretical Framework: The Nonparametric Bayesian Model (modified) is the name of the proposed model suggested by Maulidi et al. (2021) and it consists of six variables which are regional risk, cultivation time period, land area, claim frequency, discount eligibility (local variable) and premium. Discount eligibility variable is introduced to encourage right farming practices among farmers.   Design/methodology/approach: Descriptive research method is used in this study as it is used to accurately represent the characteristics of a group of items. The population for this study is 943 respondents. The entire dataset is used for in-depth and accurate analysis. Five Artificial Intelligence models (Machine Learning models) are proposed for crop insurance premium prediction and they are Ada Boost Regressor, Gradient Boosting Regressor, Extra Trees Regressor, Support Vector Regressor and K-Neighbors Regressor. Among them Gradient Boosting Regression model has given the highest accuracy. Thus, Gradient Boosting Regression model is the most suitable model to be recommended for crop insurance premium prediction.   Findings and Suggestions: Regional risk, land area, claim frequency and cultivation time period is the order of independent variables from highest to least in terms of regression coefficient. This relative importance helps Non-Banking Financial Companies (NBFCs) to suggest farmers that they should concentrate most on the regional risk or chances of crop failure in a particular region in which they are doing agriculture and least on the cultivation time period of a crop or the season in which a crop is cultivated. Two suggestions for future researchers are to extend this research work to other parts of Tamil Nadu and to apply hybrid machine learning techniques to the proposed model.   Practical Implication: Unlike the existing formula-based traditional method used for calculating crop insurance premium, artificial intelligence models (machine learning models) can automatically learn the changes that take place with respect to the nature of variables in the proposed model and improve its accuracy based on new data. Hence, the crop insurance premium suggested by the most accurate model among the artificial intelligence models used in this study will be fair to both NBFCs and farmers. Here, fair means moderate. On the other hand, the crop insurance premium suggested by the existing formula-based method may not be fair in the long term as they cannot automatically learn the changes that take place with respect to the nature of variables in the proposed model and improve.   Originality/value: In this research article, the relative importance of independent variables in the proposed model is determined and it helps NBFCs to suggest farmers that they should concentrate most on the region they are doing agriculture and least on the cultivation time period of a crop. Additionally, a machine learning model which can automatically learn and improve itself is used and hence the crop insurance premium predicted by it will be fair. Finally, the entire population containing 943 respondents details is analysed

Science Maps of Global and Indian Wildlife Forensics: A Comparative Analysis

Science map is a useful tool to understand the structure of a discipline, research networks and collaborations. Wildlife forensics is an emerging field of Forensic Sciences, where science is applied to legal cases involving wildlife. This study is aimed at creating science maps of Wildlife Forensics, both at global level and regional (i.e. India) level using PubMed database. A total of 303 records pertaining to global and 29 records pertaining to India published between 2001 and 2015 are obtained from the PubMed. These bibliometric data are analysed and maps are constructed using MS-Excel spreadsheets, VOSviewer and Pajek software. The study shows the global Wildlife Forensics literature growth showed exponential trend while the contemporary Indian literature showed linear growth trend. Globally A.M. Linacre and N. Mukaida share the first rank while among the Indian authors S.P. Goyal receives the first place. The degree of collaboration is more than 0.9. The journal Forensic Science International is the top ranking journal both internationally and nationally. The research trends in Wildlife Forensics are also found from the study

Science Maps of Global and Indian Wildlife Forensics: A Comparative Analysis

Science map is a useful tool to understand the structure of a discipline, research networks and collaborations. Wildlife forensics is an emerging field of Forensic Sciences, where science is applied to legal cases involving wildlife. This study is aimed at creating science maps of Wildlife Forensics, both at global level and regional (i.e. India) level using PubMed database. A total of 303 records pertaining to global and 29 records pertaining to India published between 2001 and 2015 are obtained from the PubMed. These bibliometric data are analysed and maps are constructed using MS-Excel spreadsheets, VOSviewer and Pajek software. The study shows the global Wildlife Forensics literature growth showed exponential trend while the contemporary Indian literature showed linear growth trend. Globally A.M. Linacre and N. Mukaida share the first rank while among the Indian authors S.P. Goyal receives the first place. The degree of collaboration is more than 0.9. The journal Forensic Science International is the top ranking journal both internationally and nationally. The research trends in Wildlife Forensics are also found from the study

PerfBound: Conserving Energy with Bounded Overheads in On/Off-Based HPC Interconnects

Energy and power are key challenges in high-performance computing. System energy efficiency must be significantly improved, and this requires greater efficiency in all subcomponents. An important target of optimization is the interconnect, since network links are always on, consuming power even during idle periods. A large number of HPC machines have a primary interconnect based on Ethernet (about 40 percent of TOP500 machines), which, since 2010, has included support for saving power via Energy Efficient Ethernet (EEE). Nevertheless, it is unlikely that HPC interconnects would use these energy saving modes unless the performance overhead is known and small. This paper presents PerfBound, a self-contained technique to manage on/off-based networks such as EEE, minimizing interconnect link energy consumption subject to a bound on the performance degradation. PerfBound does not require changes to the applications and it uses only local information already available at switches and NICs without introducing additional communication messages, and is also compatible with multi-hop networks. PerfBound is evaluated using traces from a production supercomputer. For twelve out of fourteen applications, PerfBound has high energy savings, up to 70 percent for only 1 percent performance degradation. This paper also presents DynamicFastwake, which extends PerfBound to exploit multiple low-power states. DynamicFastwake achieves an energy-delay product 10 percent lower than the original PerfBound techniqueThis research was supported by European Union’s 7th Framework Programme [FP7/2007-2013] under the Mont-Blanc-3 (FP7-ICT-671697) and EUROSERVER (FP7-ICT-610456) projects, the Ministry of Economy and Competitiveness of Spain (TIN2012-34557 and TIN2015-65316), Generalitat de Catalunya (FI-AGAUR 2012 FI B 00644, 2014-SGR-1051 and 2014-SGR-1272), the European Union’s Horizon2020 research and innovation programme under the HiPEAC-3 Network of Excellence (ICT-287759), and the Severo Ochoa Program (SEV-2011-00067) of the Spanish Government.Peer ReviewedPostprint (author's final draft

Sea urchin diversity and its resources from the Gulf of Mannar

Gulf of Mannar is the richest marine biodiversity hotspot along the Southeast coast of India, encompassing the territorial waters from Dhanushkodi in the north to Kanyakumari in the south. It has a chain of 21 islands, located 2 to 10 km from the mainland along the 140 km stretch between Thoothukudi and Rameswaram. The area of Gulf of Mannar under the Indian EEZ is about 15,000 km2 where commercial fishing takes place only in about 5,500 km2 and that too only up to a depth of 50m. This marine ecosystem holds nearly 117 species of corals, 441 species of fin-fishes, 12 species of sea grasses, 147 species of seaweeds, 641 species of crustaceans, 731 molluscan species (Kumaraguru, 2006). There are around 950 species of sea urchin in class Echinoidea which comes under two subclasses found around the world’s oceans

Multi-Walled Carbon Nanotubes Percolation Network Enhanced the Performance of Negative Electrode for Lead-Acid Battery

The discharge performance of lead-acid battery is improved by adding multi-walled carbon nanotubes (MWCNTs) as an alternate conductive additive in Negative Active Mass (NAM).We report thatMWCNTs added to the negative electrode, exhibits high capacity, excellent cycling performances at 10-h rate, high rate partial state of charge (HRPSoC) cycling and various rates of discharge. It significantly reduces the irreversible lead sulfate on the NAM, increases the active material utilization and improves the electrode performance. The improvement of capacity and cyclic performance of the cell is attributed to the nanoscale dimension of the MWCNTs as additive. Subsequent characterization using high resolution transmission electron microscopy and scanning electron microscopy were carried out to understand the influence of MWCNTs on the negative electrode of lead-acid battery