ચારણી સાહિત્યમાં શિવનું નિરૂપણઃ એક અધ્યયન

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Alice in Wonderland v. CLS Bank: The Supreme Court\u27s Fantastic Adventure Into Section 101 Abstract Idea Jurisprudence

This Article proposes a solution to the current problems surrounding section 101 and patent-eligibility. Specifically, it advocates for an amendment to section 101 of the Patent Act that eliminates the abstract idea exception when conducting a patent eligibility analysis. This approach has several advantages, including the fact that judges no longer need to provide logically contortioned explanations as to why one idea is abstract and another is not. Nor will judges have to decide whether an abstract idea can still be patent eligible by virtue of being an inventive concept of an abstract idea. Part II of this Article reviews (a) the Constitutional and statutory framework for patent protection; (b) Supreme Court precedent that first gave life to the abstract idea exception; and (c) subsequent decisions that have struggled to apply ostensibly clear precedent. Part III (a) recaps the latest abstract idea decision from the Supreme Court, Alice v. CLS Bank, and (b) examines key post-Alice Federal Circuit decisions. Part IV notes the problems associated with current abstract idea jurisprudence. This section also proposes that amending the Patent Act to eliminate any inquiry into whether an idea is abstract would be beneficial and extinguishes the problems identified. Moreover, it argues that such an amendment would not lead to the proliferation of unwarranted patents, as proper application of section 103 would serve as a meaningful bar to patent issuance

The Prospects for Hybrid Electric Vehicles, 2005-2020: Results of a Delphi Study

The introduction of Toyota's hybrid electric vehicle (HEV), the Prius, in Japan has generated considerable interest in HEV technology among US automotive experts. In a follow-up survey to Argonne National Laboratory's two-stage Delphi Study on electric and hybrid electric vehicles (EVs and HEVs) during 1994-1996, Argonne researchers gathered the latest opinions of automotive experts on the future ''top-selling'' HEV attributes and costs. The experts predicted that HEVs would have a spark-ignition gasoline engine as a power plant in 2005 and a fuel cell power plant by 2020. The projected 2020 fuel shares were about equal for gasoline and hydrogen, with methanol a distant third. In 2020, HEVs are predicted to have series-drive, moderate battery-alone range and cost significantly more than conventional vehicles (CVs). The HEV is projected to cost 66% more than a $20,000 CV initially and 33% more by 2020. Survey respondents view batteries as the component that contributes the most to the HEV cost increment. The mean projection for battery-alone range is 49 km in 2005, 70 km in 2010, and 92 km in 2020. Responding to a question relating to their personal vision of the most desirable HEV and its likely characteristics when introduced in the US market in the next decade, the experts predicted their ''vision'' HEV to have attributes very similar to those of the ''top-selling'' HEV. However, the ''vision'' HEV would cost significantly less. The experts projected attributes of three leading batteries for HEVs and projected acceleration times on battery power alone. The resulting battery packs are evaluated, and their initial and replacement costs are analyzed. These and several other opinions are summarized

Privacy Preservation using T-Closeness with Numerical Attributes

Data mining is a process that is used to retrieve the knowledgeable data from the large dataset. Information imparting around two associations will be basic done a large number requisition zones. As people are uploading their personal data over the internet, however the data collection and data distribution may lead to disclosure of their privacy. So, preserving the privacy of the sensitive data is the challenging task in data mining. Many organizations or hospitals are analyzing the medical data to predict the disease or symptoms of disease. So, before sharing data to other organization need to protect the patient personal data and for that need privacy preservation. In the recent year�s privacy preserving data mining has being received a large amount of attention in the research area. To achieve the expected goal various methods have been proposed. In this paper, to achieve this goal a pre-processing technique i.e. k-means clustering along with anonymization technique i.e. k-anonymization and t-closeness and done analysis which techniques achieves more information gain

ENHANCEMENT OF DISSOLUTION RATE OF MODAFINIL USING SOLID DISPERSIONS WITH POLYETHYLENEGLYCOLS

Solid  dispersions  (SDs)  of  modafinil  (MDF)  were  prepared using polyethyleneglycols (PEGs), in 1;1, 1;2 and 1;4 proportions by  fusion,  solvent  evaporation  and  physical  mixing  method. Differential  scanning  calorimetry  (DSC)  and  X-ray  powder diffractometry (XRD)  were  used to examine the physical state  of the  drug.  The  data  from  the  XRD  showed  that  the  drug  was converted  to  amorphous  form  as  the  number  and  intensity  of peaks  were  decreased  in  solid  dispersion  as  compared  to  pure drug and physical mixture of drug and carrier. DSC thermograms also  confirmed  the  change  in  physical  state  of  the  drug  as  the peaks were altered or disappeared. With the  highest  ratio of the carriers (1:4), the drug  solubility was enhanced by 38.68, 34.78 and 9.29 folds in solvent evaporation, fusion and physical mixing methods  respectively.  Solid  dispersion  batch  S6  containing drug:PEG6000  in  1:4,  was  selected  to  be  formulated  as  tablet (batch  TS6)  and  evaluated  for in  vitro drug  dissolution  &  six month  stability.  An  increased  dissolution  rate  of  modafinil  was observed  from  SDs  and  PMs,  as  compared  to  pure  crystalline drug.  The  dissolution  rate  of  modafinil  from  its  PMs  or  SDs increased with an increasing amount of polymer.Key  words:  Fusion,  solvent  evaporation,  physical  mixture,  in  vitro dissolution, characterization.

Applications of Support Vector Machines as a Robust tool in High Throughput Virtual Screening

Chemical space is enormously huge but not all of it is pertinent for the drug designing. Virtual screening methods act as knowledge-based filters to discover the coveted novel lead molecules possessing desired pharmacological properties. Support Vector Machines (SVM) is a reliable virtual screening tool for prioritizing molecules with the required biological activity and minimum toxicity. It has to its credit inherent advantages such as support for noisy data mainly coming from varied high-throughput biological assays, high sensitivity, specificity, prediction accuracy and reduction in false positives. SVM-based classification methods can efficiently discriminate inhibitors from non-inhibitors, actives from inactives, toxic from non-toxic and promiscuous from non-promiscuous molecules. As the principles of drug design are also applicable for agrochemicals, SVM methods are being applied for virtual screening for pesticides too. The current review discusses the basic kernels and models used for binary discrimination and also features used for developing SVM-based scoring functions, which will enhance our understanding of molecular interactions. SVM modeling has also been compared by many researchers with other statistical methods such as Artificial Neural Networks, k-nearest neighbour (kNN), decision trees, partial least squares, etc. Such studies have also been discussed in this review. Moreover, a case study involving the use of SVM method for screening molecules for cancer therapy has been carried out and the preliminary results presented here indicate that the SVM is an excellent classifier for screening the molecules

Polarographic Study of Complexation of BP+ with Schiff Bases In Acetate Buffer

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