Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. ...Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.
With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional ...environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.
The current research project investigates the correlation between economic growth, government spending, and public revenue in seventeen Indian states spanning the years 1990 to 2020. An analysis of ...the relationship between key fiscal policy variables and economic growth was conducted utilising a panel data approach, the Generalised Method of Moments (GMM), and fully modified Ordinary Least Squares (FMOLS & DOLS) estimation. In our investigation, we assessed the impacts of non-tax revenue, development plan expenditure, tax revenue, and development non-plan expenditure on (i) the net state domestic product (NSDP) and (ii) the NSDP per capita. The findings indicate that the selected fiscal variables are significantly related. The results indicate that expeditious expansion of the fiscal sector is obligatory to stimulate economic growth in India and advance the actual development of the economies of these states.
Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for ...supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.
In the present study, a sensitive LC-MS/MS method was developed and validated to measure pioglitazone (PGZ) concentrations in rat plasma and tissues. The chromatographic separation was achieved by ...using a YMC Pro C
18
column (100 mm × 4.6 mm, 3μ) with a mobile phase consisting of formic acid (0.1% v/v) and acetonitrile (5 : 95) at a flow rate of 0.7 mL min
−1
and injection volume of 10 μL (IS: rosiglitazone). Mass spectrometric detection was done using triple quadrupole mass spectrometry using the ESI interface operating in a positive ionization mode. The developed method was validated over a linearity range of 1-500 ng mL
−1
with detection and a lower quantification limit of 0.5 ng mL
−1
and 1 ng mL
−1
. The method accuracy ranged from 95.89-98.78% (inter-day) & 93.39-97.68% (intra-day) with a precision range of 6.09-8.12% for inter-day & 7.55-9.87% for intra-day, respectively. The PGZ shows the highest
C
max
of 495.03 ng mL
−1
in plasma and the lowest
C
max
, 24.50 ± 2.71 ng mL
−1
in bone. The maximum
T
max
of 5.00 ± 0.49 h was observed in bone and a minimum of 1.01 ± 0.05 h in plasma. The AUC
(0-24 h and 0-
∞
)
values are highest in plasma (1056.58 ± 65.78 & 1069.38 ± 77.50 ng h
−1
mL
−1
) and lowest in brain (166.93 ± 15.70 &167.12 ± 16.77 ng h
−1
mL
−1
), and the
T
1/2
was highest in plasma (5.62 ± 0.74 h) and lowest in kidney (2.78 ± 0.19). The developed method was successfully used to measure the PGZ pharmacokinetic and tissue distribution. Further, the developed method could be utilized for validating target organ (adipose tissue) specific delivery of PGZ (nano-formulations) in addition to conventional dosage forms.
The developed method was investigated for target and off-target distribution of pioglitazone and could be applied to validate the site-specific delivery systems.
Increasing demand for fast depleting diesel in various transport applications with a rise in vehicular exhaust emissions led many countries to research on alternative economic fuels. This paper deals ...with the study of compressed natural gas (CNG) and Schleicher oleosa oil methyl ester (SOME) with diesel as pilot fuel and triacetin as an additive on the emission, combustion and performance characteristics of a four stroke, single cylinder, common rail direct injection diesel engine working at a constant speed and varying operating scenarios. Majority of the input energy is provided by CNG when triacetin is used in pilot operation rather than diesel. The experimental study revealed that, as compared to traditional CNG + diesel (dual fuel), CNG + triacetin combination the number of harmful pollutants like smoke (5.38%), hydrocarbon (6.39%), carbon monoxide (10.24%) and oxides of nitrogen, has reduced to a considerable extent and there was a commendable improvement in the brake thermal efficiency by 8.8%. A trade-off study conducted concludes that the low emission high-performance paradox can be resolved through the dual-fuel operation with CNG + triacetin blend. So, we can summarize that when CNG and triacetin additives are blended together the combustion and performance of the engine was improved considerably and pollutant emissions were decreased.
•Diesel blending with SOME and Triacetin as pilot fuels in the dual fuel engine with CNG were investigated.•Schleichera oleosa oil is identified as a novel feedstock.•Triacetin + CNG can improve the NOx-CO trade off.•NOx, CO and HC emissions were found to be lower in triacetin + CNG while BTE increased to 7.8%.
The IPAA has been successful in restoring intestinal continuity and preserving continence in the majority of patients requiring a proctocolectomy. However, a subset of individuals experience ...significant complications that might result in pouch failure. The conversion of the J-pouch to a continent ileostomy pouch represents a significant surgical procedure. In this article, we discuss the indications and contraindications, present the technical principles applied for the conversion, and describe the outcomes of such conversion in the literature.
The main objective during the conversion of the J-pouch to a continent ileostomy is the creation of a sufficiently sized reservoir with a high-quality valve mechanism while preserving as much small bowel as possible.
The conversion of the J-pouch to a continent ileostomy represents a significant surgical procedure. When performed in centers of expertise, it can be a good option for patients who otherwise will require an end ileostomy. Indications for conversion include most cases of J-pouch failure, with a few important exceptions. See video from symposium .
PurposeOne in every four graduates of the world will be the product of Indian higher education system by the year 2030 as per a report issued by the FICCI (Federation of Indian Chambers of Commerce ...and Industry) in 2015. This brings out the growing significance of higher education sector and purpose of the study. The present study tries to explore the relationship between intellectual capital of universities and their performance.Design/methodology/approachStructural equation modeling (SEM) was applied on the dataset of 590 respondents, and the suggested model reiterate that human capital, organizational capital and relational capital have a significant influence on a university's performance.FindingsHuman capital, organizational capital and relational capital have a significant influence on a university's performance. The study strongly recommends that factors like research facilitation, quality of work life, knowledge sharing, industry academia relationship and information disclosure have a strong influence on performance.Originality/valueNot just India, but policymakers across Brazil, Russia, India, China and South Africa (BRICS) can strategize around intellectual capital to give a push to the fast-growing higher education sector.
•Micromechanics based approach to determine the transition triaxiality at which the damage mechanism changes is provided.•Factors that influence transition triaxiality are discussed.•Qualitative ...computational failure locus is developed using micromechanics.•A calibrated fracture locus for ASTM A992 steels is provided.•Calibrated fracture locus is used to predict fracture in ASTM A992 specimens.
A computational fracture locus is developed and used to predict the ductile fracture in ATSM A992 steels in this study. The fracture locus is obtained by performing micromechanical analyses on the computational cells by deforming computational cells along paths of predefined stress states described by two dimensionless stress-state parameters: stress triaxiality (Tσ) and Lode parameter (L). The microscopic damage mechanisms at different stress-states are demonstrated. The microscopic damage mechanism is observed to change from predominantly microvoid elongation to microvoid dilation at transition triaxiality of Tσ=0.75 for ASTM A992 steels. This transition stress triaxiality is found to be dependent on hardening and microstructural properties of the matrix. Also, at low triaxiality, the Lode parameter is found to have a significant effect on the microvoid elongation and dilation for ASTM A992 steels. The computational fracture locus which is a function of triaxiality and Lode parameter proposed for ASTM A992 steels is implemented in the finite element program ABAQUS® as a ductile fracture criterion. This fracture model is validated using the existing experimental data on axisymmetrically notched specimens and new data on plate with holes and notches made of ASTM A992 steels. The procedure prescribed to develop the fracture criterion in this manuscript is generic and can be used for other metals whose hardening and microstructural properties are known.
This paper empirically examines whether integrating entrepreneurial abilities with the theory of perceived behaviour positively influences Sustainable-Development-Goal-8-driven sustainable ...entrepreneurial intentions (SDG-8 SEIs). The data used in this study were gathered from 540 students from top-ranked Indian engineering colleges that offer entrepreneurship courses and have access to company incubators. According to the theory of planned behaviour (TPB), perceived behavioural control, subjective norms, and entrepreneurial drive are the three elements of perceived entrepreneurial behaviour. The TPB’s dimensions in this study have entrepreneurial competencies as their antecedents. Cognitive competency, risk propensity, and social competency and resilience are antecedents of the TPB’s dimensions. One entrepreneurial viewpoint uses sustainable UNDP-SDG-8 as a metric for assessing intentions; its objectives are the promotion of inclusive and sustainable economic growth, full and productive employment, and decent work for all. This study used partial least squares structural equation modelling (PLS-SEM). According to the findings, engineering students in India are more likely to have entrepreneurial-focused intentions based on sustainability if they adhere to the TPB’s dimensions along with additional constructs. Using an expanded TPB model, we show that the TPB has learnable and stimulating antecedents, with these having a positive effect on SDG-8 SEIs, thus extending entrepreneurial activity in India. Policymakers, universities, and students will find these results very intriguing. The TPB’s dimensions and three additional dimensions are proposed as antecedents in a new conceptual model aimed at sustainable entrepreneurship in this study.
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