Institute of Technical Education and Research

The flipped classroom is an innovative pedagogy that shifts content delivery outside the classroom, utilizing in-class time for interactive learning. The pre-class and in-class activities in this framework encourage individualized learning and collaborative problem-solving among students, fostering engagement. The Innovative Flipped Learning Instruction Project (IFLIP) conducted faculty development workshops over four years, guiding STEM faculty in integrating flipped teaching (FT) into their courses. The research aimed to assess its impact on pedagogical practices, explore its effectiveness, and provide a framework to implement FT across multiple institutions. It sought to evaluate the experiences of these educators throughout the transitional period of instructional change. In the fourth year of this project, a symposium was organized for IFLIP participants to share their experiences and findings concerning FT. This symposium helped promote collaboration among IFLIP participants and faculty interested in FT to disseminate participants' knowledge and experiences in implementing FT strategies. A survey conducted at the end of the symposium indicated that faculty participants with FT experience continued to embrace this pedagogy, and the new adopters expressed intentions to incorporate it into their courses. The survey revealed positive responses: 93% of respondents plan to integrate FT methods in future classes, 90% gained new information from the symposium and intend to implement it, and 91% are likely to recommend FT to colleagues. Ultimately, the symposium underscored the transformative impact of FT in empowering educators to deepen students' conceptual understanding, emphasizing the significance of this pedagogical approach in advancing the quality of education.

Brain tumor detection and segmentation from multi-parametric magnetic resonance (MR) scans are crucial for the prognosis and treatment planning of brain tumor patients in current clinical practice. With recent technological advancements, artificial intelligence-based deep learning has proven its indispensable image analysis capability in the most challenging tasks. This study proposes an automated WU-Net + + deep learning model for brain tumor segmentation using multiparametric structural MR scans obtained from the BraTS 2018 dataset. The model was validated through cross-dataset testing for intracranial hemorrhage (ICH) classification (ATLAS V2.1 dataset) and multi-organ segmentation from abdominal images of TCIA and BTCV datasets. The WU-Net++ model, a novel version of U-Net, was developed by adjusting its pooling operation as a weighted function of max and average pooling for brain tumor segmentation. The proposed model (WU-Net + +) achieved an F1 score of 0.94 ± 0.124, a dice score of 0.91 ± 0.132, and an AUC value of 0.915 for whole tumor segmentation. The model also achieved a high accuracy of 0.9949 ± 0.121 in ICH classification and dice scores of 0.912 ± 0.21, 0.844 ± 0.25, and 0.893 ± 0.17 for spleen, esophagus, and portal and splenic vein segmentation, respectively. Our study revealed that WU-Net + + has significant potential to improve the accuracy of segmentation and could be an effective method in the era of precision medicine.

The research involves synthesizing 1:1 salts of Pipemedic acid (PMA) with oxalic acid (OA), salicylic acid (SA), and p-Toluene sulfonic monohydrate (BS) using a slow evaporation method. Many characterization techniques, including FT-IR, DSC, Single XRD, and DFT calculations, were employed to analyze the salts’ structural and physicochemical properties. The proton is transferred from oxalic, salicylic acid, and p-toluene sulfonic monohydrate to pyridine nitrogen of PMA. The salt 1OA, crystallizes in the monoclinic space group P 21/c, with a = 9.923(3) Å a = 90°, b = 9.443(3) Å b = 92.470(10)°and c = 18.248(5) Å g = 90° and volume = 1708.3(9) Å3and Z = 4. The salt 2SA crystallizes in the monoclinic space group P 21/c, with a = 6.8877(3) Å a = 90°.b = 13.9149(6) Å b = 98.092(2)° and c = 21.5313(10) Å g = 90° with volume = 2043.05(16) Å3 and Z = 4.The salt 3BS crystallizes in the monoclinic space group P 21/c, with, a = 9.3352(4) Å a = 90°, b = 12.7754(5) Å b = 97.722(2)°, c = 19.5462(8) Å g = 90°,with volume = 2309.96(16) Å3 and Z = 4. Supramolecular centrosymmetric ring motifs are formed by N–H···O hydrogen bonds between protonated nitrogen of the pyridone ring and the carboxylic O atom of the oxalate ion, in both 1OA and 2SA. The dihedral angles of 1OA, 2SA, and 3BS are found to be 43.63°, 88.19°, and 53.89° respectively. The Hirshfeld surfaces and the related 2D fingerprint plots were explored which uncovered that more than two-thirds of close contacts were related to H⋯H, C–H, N–H, and C–C bonding interactions whereas in 3BS, the structure is stabilized by N–H···O and N–H···S hydrogen bonding interactions. These weak associations assume a significant role in crystal packing as revealed by the Hirshfeld surfaces and the related 2D fingerprint plots.

Rhodopsin is a G protein-coupled receptor (GPCR) present in the rod outer segment (ROS) of photoreceptor cells that initiates the phototransduction cascade required for scotopic vision. Due to the remarkable advancements in technological tools, the chemistry of rhodopsin has begun to unravel especially over the past few decades, but mostly at the ensemble scale. Atomic force microscopy (AFM) is a tool capable of providing critical information from a single-molecule point of view. In this regard, to bolster our understanding of rhodopsin at the nanoscale level, AFM-based imaging, force spectroscopy, and nano-indentation techniques were employed on ROS disc membranes containing rhodopsin, isolated from vertebrate species both in normal and diseased states. These AFM studies on samples from native retinal tissue have provided fundamental insights into the structure and function of rhodopsin under normal and dysfunctional states. We review here the findings from these AFM studies that provide important insights on the supramolecular organization of rhodopsin within the membrane and factors that contribute to this organization, the molecular interactions stabilizing the structure of the receptor and factors that can modify those interactions, and the mechanism underlying constitutive activity in the receptor that can cause disease.

Oral cancer is a prevalent malignancy that affects the oral cavity in the region of head and neck. The study of oral malignant lesions is an essential step for the clinicians to provide a better treatment plan at an early stage for oral cancer. Deep learning based computer-aided diagnostic system has achieved success in many applications and can provide an accurate and timely diagnosis of oral malignant lesions. In biomedical image classification, getting large training dataset is a challenge, which can be efficiently handled by transfer learning as it retrieves the general features from a dataset of natural images and adapted directly to new image dataset. In this work, to achieve an effective deep learning based computer-aided system, the classifications of Oral Squamous Cell Carcinoma (OSCC) histopathology images are performed using two proposed approaches. In the first approach, to identify the best appropriate model to differentiate between benign and malignant cancers, transfer learning assisted deep convolutional neural networks (DCNNs), are considered. To handle the challenge of small dataset and further increase the training efficiency of the proposed model, the pretrained VGG16, VGG19, ResNet50, InceptionV3, and MobileNet, are fine-tuned by training half of the layers and leaving others frozen. In the second approach, a baseline DCNN architecture, trained from scratch with 10 convolution layers is proposed. In addition, a comparative analysis of these models is carried out in terms of classification accuracy and other performance measures. The experimental results demonstrate that ResNet50 obtains substantially superior performance than selected fine-tuned DCNN models as well as the proposed baseline model with an accuracy of 96.6%, precision and recall values are 97% and 96%, respectively.

This study investigates the monitoring of corrosion in the distribution network pipeline in Patna (Bihar), India, using a weight loss method and an image processing technique. The seven coupons were injected into the pipeline of the distribution network and ejected after 45, 90, 135, 180, 225, 270, and 315 days. The corrosion rate was calculated by weighing the initial and final weight of coupons. It was found to be 0.035, 0.078, 0.048. 0.035, 0.028, 0.052, and 0.039 mm/year during 45, 90, 135, 180, 225, 270, and 315 days, respectively. Scaling rate found at 45, 90, 135, 180, 225, 270, and 315 days was 0.006, 0.037, 0.042, 0.049, 0.011, 0.018, and 0.033 mm/year, respectively. Surface analysis was performed using the scanner method and MATLAB software. Coupons were first scanned using a scanner adjusted to 4800 ppi. A scanned image was imported into MATLAB. An algorithm was used based on color analysis. The output of a text file was generated that gave the corrosion pit in terms of pixels. The MATLAB analysis shows that the surface corrosion area sum of both sides of the coupon was 6.24, 6.39, 6.49, and 6.48 cm2 in 45, 90, 135, and 180 days, respectively. This study concludes that scale formation on the pipe surface decreased the corrosion rate. Corrosion and scaling were found in the opposite trend. SEM analysis shows that pitting corrosion was increasing with increasing duration of time. Physicochemical analysis shows that the water was alkaline. The presence of a greater concentration of hardness, alkalinity, and less concentration of chloride, sulfate, and DO water was found to have a scaling tendency. XRD analysis of iron dust represents the presence of salts in water, which is responsible for the scaling behavior of water.

Renewable energy is a perennial form of energy that provides an imperishable and sustainable environment for living beings. For establishing an unconventional society, there is a considerable need of making the power generation to the level of extent in the revitalization of technology. In this chapter, authors proposed such a system that is PV‐Wind Hybrid Power generation system. As for extracting and maximizing the output of the proposed configuration in the photovoltaic (PV) system, use of Neural Network Predictive and Adaptive Neuro‐Fuzzy Inference System (ANFIS) Controllers as maximum power point tracking (MPPT) is implemented. Further, for Wind Energy Conversion system, PMSG‐Based Wind Turbine is enabled, and for intensifying the generation of the system, the pitch angle control is actualized by the combination of NN‐based NARMA‐L2, PI, and Fuzzy Logic Controllers which has elevated the processing of the performance of the system. Also, for assuring the distortion less power generation the ingenious Dynamic Voltage Restorer is realized in the Hybrid Power System, and it is controlled by skillful ANFIS and PI Controller for maintaining the consistent output power for the virtuous and vigorous system. The simulations were performed in MATLAB/SIMULINK, and the results‐oriented performance is decisively validated.

The growth and popularity of virtual science laboratories have skyrocketed all over the world in recent years due to the covid-19 pandemic. India has also adopted the idea of remote learning, but the overall utilization of virtual science labs to complement remote science learning has been slow and inadequate so far. This article aims to introduce the Indian audience to the world of three relatively new virtual laboratories (Beyond Labz, Labster, and Praxilabs) that can be useful for undergraduate science education. The salient features, experimental demonstration, and merits and demerits of these three platforms have been discussed in this article.

The enormous benefits and applications of Image classification and recognition are umpteen. Machine learning algorithms and Deep Neural Networks are like windfall to fathom the objective proficiently in streamlined manner. The prevalent improvement in this technology is that these networks do not call for any prior blueprints in terms of algorithms as prerequisites. The presented paper is an attempt to create a Convolutional Neural Network from scratch to classify the images from the well-known dataset – Cats and Dogs into their relevant baskets. Manifold open source accessible approaches to amplify the efficiency of the network are no more onerous. Further, data augmentation technique boosts the efficiency tremendously by extending the dataset with reoriented features from the same images. To untangle the same problem, Transfer Learning is also a compelling technique in which all the layers, neurons in each layer, weights of each neuron and all other parameters are predefined and we can amend the output layer as per the classes in the respective problem statement. In the present paper, we have tried to obtain a comparable efficiency with a significant reduction in parameters.

The self-standing computers embodies a distributed system, intertwined by a concatenated web associate degreed endowing the software of districuted system. The hardware fragment of distributed system with remodelling the size of some workstations integrated by one native space network with bazillions of computers twinned with multifold wide area networks. The potential of distributed system is unveiled which supports the trade-off of reliability and cost buttoned up with comprehensive search mechanisms. The diversified attributes like the reliability of execution and reliability, price of execution and communication are the principal components and portrayed in matrices, explicitely the CRM(,), ERM(,), CCM(,), electronic welfare(,). Reorientation and conversion of these matrices is consistant with the conjucted tasks. Every task fusion evaluates the reliability of the distributed computer contiguous with the execution cost as well as the communication cost. The optimum indisputable response of distributed computing system is bagged when the reliability aspect is modulated for all the blends of tasks. A live of elongated performance is achieved finally beyond shadow of doubt.

In this study the effects of modification with creation of grooves as well as the use of nanoparticles with the base fluid has been considered. The main objective during the investigation is to predict the effect of enhancement in thermal efficiency with variation in thermal as well as hydraulic performance of the system. The use of nanoparticles improves the thermal conductivity as well as the hydraulic performance of the PTCs. It is seen that at lower values of Pop (Pop less than 0.20); the difference between the thermal efficiency (□) of conventional parabolic trough collector (CPTC) and parabolic trough collector with internal circular cut helical grooves (HG) systems are about 11.31–13.11 % higher. While the □ of HG with copper nanoparticles (HG + Cu) is the highest and lowest thermal performance index (TEI) with 60.4 % and 0.159 respectively among three PTCs. Therefore, the □ can be increased either by enhancement in thermal performance or by hydraulic performance.

Demographically India is in a very unique position compared to many other countries in the world. At present, the under-25 population in India is more than 50% of India's total population, and it is predicted that it would comprise ∼25% of the world population within the next decade. This "demographic dividend"provides India with a solid platform to explore different possibilities in the field of education and research, especially after the approval of the new National Education Policy (NEP) in 2020. NEP 2020 proposed drastic changes for both the school and higher education systems including a brand new academic structure 5 + 3 + 3 + 4, increased focus on experiential learning and higher-order thinking skills, innovative and technique-based pedagogy, new evaluation techniques, teacher training, and online learning. In light of these proposed changes in the educational system, we have discussed a brief history of chemistry education and research in India and its present scenario. We have examined the growing trend of the increasing number of students pursuing science, particularly in chemistry (PhDs in chemistry accounting for almost 20% of PhDs in science in India). We also debated the quality and impact of research publications from India. Finally, we scrutinized the challenges faced by chemistry education in India and explored different opportunities based on the new NEP to unleash the full potential of chemistry education and research in India.

This research utilizes metaheuristic optimization inspired by the Egyptian Vulture Optimization (EVO) technique. Biomedical image segregation is developed to reduce the complex association of hyperparameters of Convolutional Neural networks (CNN). The complex attributes of CNN include the type of kernel, size of the kernel, size of the batch, epoch counts, momentum, learning rate, activation function, convolution layer, and dropout. However, the life cycle of an Egyptian vulture influences the optimization technique to resolve complexity and increase the accuracy of CNN. The proposed CNN-based EVO model was evaluated in comparison to ANN-based and deep learning-based classifiers utilizing brain MRI image datasets. The results achieved have confirmed the efficiency and performance of the proposed CNN-based EVO model, in which the average detection accuracy and precision were 93% and 95%, respectively.

In the rapid world of technology, the IT Industry is growing at a rapid speed with Cloud Image Security. In the developing trends, cloud orchestration is becoming impressive and uncertain of dealing a large amount of information. In the last two decades there has been extensive development in image encryption. Chaos based image encryption techniques form framework for a faster algorithm than traditional techniques. Chaos based techniques are highly efficient in case of multimedia data. The proposed algorithm uses the modified logistic map for the generation of chaotic sequence, encrypts the image in single scan, so runs faster and makes the proper use of confusion and diffusion. It dynamically updates the key during execution. This algorithm is secure against the brute force attack and differential attack.

Predominantly, GPS-free localization algorithms are either predictive or probabilistic or range-dependent which outrage energy source and computing power of sensor-nodes. Whereas, comparatively less complex algorithms based on range-independent approaches find GPS-free approaches unfeasible due to their everlasting dependency on GPS like DV-Hop, etc. This gap motivates to propose RiL-DZ, an estimative range-independent absolute localization algorithm for GPS dead zone in mobile wireless sensor networks. RiL-DZ has two steps- ALE, and UL. In ALE, for the dead zone anchor-nodes calculate their location by measuring their respective distance and angular movement. Subsequently in UL, the absolute location of the unknown-nodes is attained by estimating their distances from the anchor-nodes of ALE step using hop parameters. Further, for precise localization, RiL-DZ considers errors in distance estimation between the node-pairs and the imprecise approximation of the location of anchor-nodes caused by error-affected displacement measurements. Furthermore, the error factors of distances calculated in between anchor-nodes and intended unknown-node are minimized using linear programming. The simulation shows RiL-DZ absorbs displacement error notably than other contenders and GPS-assisted DV-Hop throughout. The analysis-of-variance establishes that RiL-DZ depends significantly on communication range and anchor-nodes density only by 40.81% and 29.09% respectively.

Plants are the source of different lipase inhibitors and have complex matrices. Lipase inhibitors from plant matrices can be directly detected on Bioautograms. In this study, a p-Nitrophenyl butyrate based bioautography was evaluated for the identification of lipase inhibitors from unexplored plants. Most of the unexplored plants were found to be positive for the lipase inhibitory activity. Leaf extracts of Lantana camara and Sorghum bicolor showed dark blue spots, whereas the floral extract of Lantana camara and seed extract of Areca catechu showed faint blue spots on the bioautogram suggesting the presence of higher or lower levels of lipase inhibitory activities respectively. Floral extract of Delonix regia showed the lipase inhibitory activity as a faint blue spot on the bioautogram which was not detected by the spectrophotometry. Hence, false negatives and false positives can be avoided with bioautography. Bioautographic assay was successfully validated by checking the lipase inhibitory activities in the blue spots.