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The global industrial needs strongly urge for better product management and deliveries. This requires a robust structure and planning for better risk estimation, cost calculation and catering market demands. Harnessing this is significantly dependent on the challenge undertook by efficient and skilled product managers with improved team participation, promoting better change practices and ultimately achieving incremental growth. Previous researches conducted on Fortune 500s suggest that 50% of corporate programmers find it difficult to work in an agile software development model. This research document intends to provide structure and personalization to every aspiring and experienced Product Manager's learning journey. The existing product managers sometimes find it difficult to adapt to the market changes and demands. This leads to PMs wasting a lot of time in finding relevant material and organizing it to serve their needs. This document intends to utilize years of experience of seasoned PMs who can guide the aspirants in their quest by suggesting and curating tons of resources available online.

This work reports rapid, label-free and specific detection of the HER2 antigen using a gallium nitride (GaN) high electron mobility transistor (HEMT). Thiol-based chemistry has been utilized to immobilize the corresponding HER2 antibody in the sensing area of the sensor. The formation of a gold-sulfur complex has been confirmed through Raman spectroscopy, giving a peak at around a wavelength of 260 cm

Dispersion of nanofiller in the polymer matrix is a vital factor that influences the properties of the fabricated nanocomposites at the laboratory level. Characterization techniques like TEM and FESEM, having a small sample size, tend to miss out on the big picture for the analysis of mixing on a larger scale. At the industrial level, conducting such testing with varying experimental conditions is not viable in terms of both cost and time. Through this study, we propose a simple method to examine the extent of dispersion using a simple camera and employing deep learning (DL) models. For this purpose, an analogous study has been performed to study the sensitivity of the processing techniques, to better understand the findings in our previous articles. A two component-colored solution (oil–water) was utilized as a proxy for the nanofiller-polymer matrix system. Different processing methods were employed namely ultrasonication, homogenization, sequential ultrasonication and homogenization and simultaneous ultrasonication and homogenization. The variation in processing technique significantly affects the dispersion which is attributed to the different mixing mechanisms (turbulent, diffusive, and convective) incurred in these processing techniques. Inferences are withdrawn by detecting patterns in a large sample size which highlights that DL models provide us with a holistic viewpoint of real-time observations. It also ameliorates human interpretation by unraveling obscure information which can go unnoticed by human eyes.

The detection of the cancer biomarker human epidermal growth factor receptor 2 (HER-2) has always been challenging at the early stages of cancer due to its very small presence. A systematic study of biosensors to achieve optimum sensitivity is of paramount significance. Thus, in this paper, we report a simulation study and machine learning (ML) based model for the comparative analysis of indium aluminum gallium nitride (InAlGaN) and aluminum gallium nitride (AlGaN) based high electron mobility transistors (HEMTs) for the detection of HER-2. The sensing performance of the InAlGaN based HEMT exhibits 1.8 times higher sensitivity as compared to that of the AlGaN based HEMT. The presented work also provides insights into the importance of the pH of the medium of HER-2. The results produced by the developed ML-based model are in good agreement with the simulation results. The model is not only capable of predicting within the trained range but also it can predict reasonably well even beyond the range of the training data. The introduction of a ML-based model significantly reduces the computational cost, time to perform similar type of simulations and, unlike the physics-based modelling, it also eliminates the need for empirical fitting of the model parameters.

The Global Health Scenario has been changed at present due to unpredictable outbreak of pandemic novel Corona virus (SARS-Co V- 2) in the community of nation. The coronavirus has already transmitted the disease COVID -19 all over 213 countries and territories around the world. So as a resultant factor, 22,862,661 confirmed corona virus case already detected and among them 797,117 deceased cases noted as of August 21st, 2020. In spite of extensive efforts to resolve the pandemic conditions, transmission as well as their treatment modalities still now could not be established satisfyingly. The exact term Corona vi-rus is not mentioned in Ayurvedic treatise but viruses as well as any micro-organisms and their preventive measures also illustrated in Ayurvedic manuscript in Susrut Samhita Sutra Sthana as Jantu or Nisachara. Even the pandemic situation also described in Charak Samhita, Viman Sthana as the term of “Jana Pada Dhangsha”. The classical medical science Ayurveda, already proven its efficacy over flu like condition as well as viral & respiratory distress in human being is having such of similarity with Agantuja Sannipatika Roga as well as highly contagious disorder, so for the purpose of breaking the chain of Corona virus transmission in human being, Ayurvedic preventive as well as protective measures could be beneficial.

We have investigated the characteristics of pH and salinity sensor derived from the gated AlGaN/GaN high-electron mobility transistor (HEMT) structures in phosphate buffer saline (PBS) and aqueous salt solutions (NaCl + DI). In deionized (DI) water, the HEMT device showed good drain ${I}$ – ${V}$ characteristics, which is very close to the output characteristics of the typical HEMT structures subjected to the air. We observed a significant change in the output drain characteristics curves concerning to the variation in the pH values of PBS solutions, signifying the subsequent potential variation at the AlGaN surface. The output drain current recorded at ${V}_{\text {ds}}= +1$ V was linearly decremented with the pH value. A high sensitivity of $4.32~{\mu }\text{A}$ /mm-pH was obtained. These GaN HEMT structures demonstrated a quick response to the pH changes. It was also investigated that the devices were susceptible toward the aqueous salt solution (NaCl + DI). The percentage change in drain current linearly decreased with decreasing NaCl molar concentration in DI water. We have reported on the change in current with the smaller range of molar concentration of NaCl present in water. Evaluating the sensitivity and response time, we obtained a high sensitivity of 6.48 mA/mm-molar and a response time of 250–350 ms at ${V}_{\text {ds}}= +1$ V. We have also reported on the change in current with the molar concentration of NaCl present in PBS with a high sensitivity of 2.02 mA/mm-molar at ${V}_{\text {ds}}= +5$ V. These outcomes show that the AlGaN/GaN HEMTs are exceptionally promising as a high-sensitivity pH sensor and salinity sensor for biological experiments.

This research article proposes a deep learning framework that addresses two major hurdles in adopting deep learning techniques for solving physics-based problems. One is the requirement of a large data set for training the deep learning (DL) model and another is the consistency of a DL model with the physics of a phenomenon. The framework is generic that can be applied to model a phenomenon in physics if its behavior is known. A semi-supervised physics guided neural network (SPGNN) has been developed based on our framework to demonstrate the concept. SPGNN models the I– V characteristics of gallium nitride based high electron mobility transistors (GaN HEMTs). A two-stage method has been proposed to train a DL model. In the first stage, the DL model is trained via an unsupervised learning method using the analytical physics-based model of a field-effect transistor (FET) as a loss function of the DL model that incorporates the physics of the FET in the DL model. Later, the DL model is fine-tuned with a small set of experimental data in the second stage. Performance of SPGNN has been assessed on various sizes of the data set with 100, 500, 1000, 1500, and 2000 samples. SPGNN significantly reduces the training data requirement by more than 80% and provides better performance than a traditionally trained neural network (TTNN), even for the unseen test data set. SPGNN predicts 32.4% of the unseen test data with less than 1% of error and only 0.4% of the unseen test data with more than 10% of error.

Metabolic surgery is the most effective and proven therapeutic option for obesity and obesity-related complications. Recent years have seen an increment in the number of bariatric surgeries performed, ranging from purely restrictive to a combination of restrictive and malabsorptive procedures. These metabolic procedures carry substantial risks, pertaining to procedural related complications, micro-nutritional, hormonal, endocrinal, and metabolic disturbances. The above complications are likely due to the surgically induced changes in the anatomy, incurred by the patient's gastrointestinal tract. In addition to the monitoring of weight and related comorbidities outcomes, timely screening, counseling, monitoring, nutrient, and mineral supplementation are essential for the treatment and prevention of complications following bariatric surgery. Through this chapter, we aim to bring forth certain rare, yet documented case scenarios concerning the micro-nutritional, endocrine, and metabolic complications after bariatric surgery. The surgeon, treating physician, intern, and the patient must be aware of these rare complications of the surgery, for its timely diagnosis and intervention.

We have developed a fast and simplistic AlGaN/GaN high electron mobility transistor (HEMT) for the detection of heavy metals such as mercury, lead, copper and zinc in water with a dynamic range of 1 nM to 1 mM. Four device designs have been fabricated where the gate structure and the distance between the source and the drain have been varied. It is observed that the device with meander gated (MG) structure and larger sensing area showed better response. A detailed analysis of the surface of the samples has been also investigated using SEM, EDS and Raman spectroscopy which confirms the re-usability of the sensor for a longer period of time. The response of the sensor stabilizes at 35 and 27 s for mercury and copper ions respectively. The response time of the sensor also shows a concentration dependent behavior. The sensor exhibits the high sensitivities of 31, 38, 35, and 24 mV/decade [M] for mercury, copper, zinc and lead ions respectively.

Radical gastrectomy with N2 lymphadenectomy (D2 gastrectomy) has been shown to have a survival advantage in Japanese and western trials, but Indian experience is limited. A retrospective analysis of prospectively collected data of patients with carcinoma of the stomach considered for surgery from 2009 to 2014. The operative details, pathological TNM stage and survival were analysed. Total number of patients was 58, out of which 34 patients (59%) had radical gastrectomy with N2 lymphadenectomy (D2-gastrectomy), 11 (18%) patients had locally advanced disease and 13 (22%) patients had metastatic disease. Mean blood loss was 180 ml (±85 SD), and mean duration of surgery was 286 (±65 SD) minutes. Median length of hospital stay was 8 days (6–17 days), 30-day mortality was zero and a total of four patients (12%) had grades III to IV complications (Clavien-Dindo classification). The median number of lymph nodes removed was 18 (11–31). Pathological TNM stage was II in 8%, III in 47% and IV in 41%. The median survival of patients undergoing curative resection was 28 months, and it was 6 months in patients without curative resection (P

This work reports on the development of a compact GaN high-electron-mobility transistor (HEMT) based biosensor for an easy and early detection of breast cancer biomarker C-erbB2 in the human cell line. The early-stage detection process includes a reaction of antibody-antigen through the cell line culture of a patient. The developed sensing chip has a two-finger gate structure of 125 µm gate width, and a gate length of 5 µm. In order to functionalize the gold surface of the fabricated sensor, the sensor chip has been immersed into an optimized 1 M aqueous solution of thioglycolic acid at room temperature for 15 h. Various characterization methods such as I–V, EDS, and FTIR confirm the formation of the Au–S complex. The functionalized sensors have been incubated in phosphate buffer saline solution of 200 µg ml−1 C-erbB2 monoclonal antibody for 4 h. Thereafter, the C-erbB2 monoclonal antibody conjugated devices are incubated with human cancer cells positive for C-erbB2 on its cell surface. The biosensor shows a 31% change in drain current for an incubation period of 6 h. The high-resolution biosensing chip (in terms of high drain current levels of the order of mA) is unaffected by noise and eases the circuit for futuristic point of care diagnostics.

In this paper, a GaN-high electron mobility transistor (HEMT) based sensor is designed, fabricated and characterized for polar liquid sensing. The fabricated HEMT sensor chip is packaged by using low temperature co-fired ceramic (LTCC) technique. The sensor shows a typical drain current of 21.2 mA at 3.3 V. The fabricated sensor shows a percentage change of 1.78%, 2.18% and 6.3% in drain current for mercury chloride, copper chloride and sodium chloride respectively with respect to the drain current of pure water. The readout circuit shows a change of 0.15, 0.20 and 0.34 mA in drain current for acetone, water and methanol respectively and ensures the detection of different polar liquids. Due to the novel inter-digital structure, the sensor shows a sensitivity of 5.98 mA/mm/Debye at a drain voltage of 3.3 V. Sensing mainly depends on the dipole moment because the change in the dipole moment of the liquid causes a change in surface potential at the gate sensing area. This paper gives a theoretical and practical perspective on polar liquid detection using both, the sensor and system.

This paper reports on the design and fabrication of Gallium Nitride HEMTs based liquid sensor. Sensor is packaged using thick film Alumina based packaging. A drain current of 6.17 mA is measured on packaged HEMTs at 1.0 V. The packaged HEMT sensor is used for the sensing of different polar liquids namely Acetone, Water, and Methanol. It shows reduction in the drain current as the dipole moment of the liquid increases. Lowest drain current is recorded when the package is dipped into Acetone as compared to Water and Methanol. Four different designs (50_G, 50_ID, 25_G and 25_ID) of sensors are used to detect heavy metal ion Hg+2. The detection limit of design 25_G, 50_G and 50_ID is found to be in mM range. Best results are obtained on design 25 _ ID which shows a 4.8 % change in the drain current with respect to dry response for nM concentration. Not to mention, design “25_ID” is novel and has never used before for the HEMT sensing. Design is unique in the sense of having multiple gates arranged in the electrode fashion.