Research News

Soil and groundwater contamination is closely interlinked with human society because of its direct impact on population health and socioeconomic activities. The design and implementation of site remediation can be expensive, time-consuming, and may require much human effort. Emerging technologies such as Artificial Intelligence, Machine Learning, and Deep Learning have the potential to make site remediation cost-effective with reduced human effort.

Assistant Professor Dr Raviteja KVNS, Department of Civil Engineering, has received the Best Paper Award at the Fourth International Conference on Trends and Recent Advances in Civil Engineering (TRACE) 2022 for his paper Application of artificial intelligence, machine learning and deep learning in contaminated site remediation. The conference was held at Amity University, Uttar Pradesh, on October 18 and 19, 2022. His research reports the applications of AI and ML in contaminated site remediation.

Dr Raviteja’s future research plan includes studying potential applications of various AI, ML and DL techniques for Geotechnical and Geo-environmental design and testing applications so as to reduce the labours of physical and repetitive testing and associated human effort. This further improves precision as well as aids in decision-making. He has collaborated with Prof. Krishna R Reddy, University of Illinois Chicago, for this research work.

Abstract

Soil and groundwater contamination is caused by improper waste disposal practices and accidental spills, posing a threat to public health and the environment. It is imperative to assess and remediate these contaminated sites to protect public health and the environment as well as to assure sustainable development. Site remediation is inherently complex due to the many variables involved, such as contamination chemistry, fate and transport, geology, and hydrogeology. The selection of remediation method also depends on the contaminant type and distribution and subsurface soil and groundwater conditions. Depending on the type of remediation method, many systems and operating variables can affect the remedial efficiency. The design and implementation of site remediation can be expensive, time-consuming, and may require much human effort. Emerging technologies such as Artificial Intelligence, Machine Learning, and Deep Learning have the potential to make site remediation cost-effective with reduced human effort. This study provides a brief overview of these emerging technologies and presents case studies demonstrating how these technologies can help contaminated site remediation decisions.

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SRM AP proudly congratulates Dr Anil K Suresh, Associate Professor, Department of Biological Sciences and his cohort of research scholars for their rare achievement of having their paper featured in the prestigious weekly news magazine Chemical and Engineering News (ACS-C&EN). The news article titled “This catalyst is a Golden Egg“, edited by Prachi Patel highlights the innovative research conducted by Dr Anil K Suresh and his team on developing a low-cost, sustainable catalyst by infusing eggshells with gold nanoparticles that can be reused and eventually recycled.

The research paper titled Sustainable Bio-Engineering of Gold structured Wide-Area Supported Catalyst for Hand-Recyclable Ultra-Efficient Heterogeneous Catalysis (ACS Appl. Mater. Interfaces 2022, DOI: 10.1021/acsami.2c13564) highlights the team’s breakthrough advance in impregnating eggshells with gold nanoparticles to develop a cheap, and reusable ‘mega catalyst’. The research has used the robust “mega catalyst” to detoxify dye waste and run other organic reactions by dropping the eggshell catalyst into reaction solutions.

Dr Suresh and colleagues dipped waste eggshells collected from restaurants in a suspension containing gold chloride for 6 hours. Consequently, the amino acids on the shells reduced the gold ions to form crystalline gold nanoparticles. Dr Suresh attested this method to be one of the simplest syntheses of a supported catalyst that was ever reported. He emphasised that he and his colleagues have also grafted catalytic silver and copper oxide on eggshells and are now working on methods to do this for platinum and palladium.

SRM University-AP manifests pioneering advancement in innovative domains of research in the field of science and technology. The breakthrough research by Dr Anil K Suresh and his research team is of outstanding achievement and incredible addition to the institution’s laurels in the field of translational research.

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Breast cancer (BC) is one of the most common types of cancer among women with a high mortality rate. Histopathological analysis facilitates the detection and diagnosis of BC but is a highly time-consuming specialised task, dependent on the experience of the pathologists. Hence, there is a dire need for computer-assisted diagnosis (CAD) to relieve the workload on pathologists. Dr Sudhakar Tummala, Assistant Professor, Department of Electronics and Communication Engineering, has conducted breakthrough research on this domain in his paper titled BreaST-Net: Multi-Class Classification of Breast Cancer from Histopathological Images Using Ensemble of Swin Transformers published in the Q1 Journal Mathematics, having an Impact Factor of 2.6.

Abstract

Breast cancer (BC) is one of the deadly forms of cancer and a major cause of female mortality worldwide. The standard imaging procedures for screening BC involve mammography and ultrasonography. However, these imaging procedures cannot differentiate subtypes of benign and malignant cancers. Therefore, histopathology images could provide better sensitivity toward benign and malignant cancer subtypes. Recently, vision transformers are gaining attention in medical imaging due to their success in various computer vision tasks. Swin transformer (SwinT) is a variant of vision transformer that works on the concept of non-overlapping shifted windows and is a proven method for various vision detection tasks. Hence, in this study, we have investigated the ability of an ensemble of SwinTs for the 2- class classification of benign vs. malignant and 8-class classification of four benign and four malignant subtypes, using an openly available BreaKHis dataset containing 7909 histopathology images acquired at different zoom factors of 40×, 100×, 200× and 400×. The ensemble of SwinTs (including tiny, small, base, and large) demonstrated an average test accuracy of 96.0% for the 8-class and 99.6% for the 2-class classification, outperforming all the previous works. Hence, an ensemble of SwinTs could identify BC subtypes using histopathological images and may lead to pathologist relief.

A brief summary of the research in layperson’s terms

Breast cancer (BC) is the second deadliest cancer after lung cancer, causing morbidity and mortality worldwide in the women population. Its incidence may increase by more than 50% by the year 2030 in the United States. The non-invasive diagnostic procedures for BC involve a physical examination and imaging techniques such as mammography, ultrasonography and magnetic resonance imaging. However, the physical examination may not detect it early, and Imaging procedures offer low sensitivity for a more comprehensive assessment of cancerous regions and identification of cancer subtypes. Histopathological imaging via breast biopsy, even though minimally invasive, may provide accurate identification of the cancer subtype and precise localization of the lesion. However, this manual examination by the pathologist could be tiresome and prone to errors. Therefore, automated methods for BC subtype classification are warranted.

Deep learning has revolutionised many areas in the last decade, including healthcare for various tasks such as accurate disease diagnosis, prognosis, and robotic-assisted surgery. There were studies based on deep convolutional neural networks (CNN) for detecting BC using the aforementioned imaging procedures. However, CNNs exhibit inherent inductive bias and are variant to translation, rotation, and location of the object of interest in the image. Therefore, image augmentation is generally applied while training CNN models, although the data augmentation may not provide expected variations in the training set. Hence, self-attention based deep learning models that are more robust towards the orientation and location of an object of interest in the image are rapidly growing.

SwinTs are an improved version of earlier vision transformer (ViT) architecture and are hierarchical vision transformers using shifted windows that work based on self-attention. For efficient modelling, self-attention within local windows was proposed and computed, and to evenly partition the image, the windows are arranged in a non-overlapping manner. The window-based self-attention has linear complexity and is scalable. However, the modelling power of window-based self-attention is limited because it lacks connections across windows. Therefore, a shifted window partitioning approach that alternates between the partitioning configurations in consecutive Swin transformer blocks was proposed to allow cross-window connections while maintaining the efficient computation of non-overlapping windows. The shifted window scheme in Swin transformers offers increased efficiency by restricting self- attention computation to local windows that are non-overlapping while also facilitating a cross-window connection. Overall, the SwinT network’s performance was superior to that of the standard ViTs.

Therefore, the paper analyses the ability of an ensemble of Swin transformer models (BreaST-Net) for the automated multi-class classification of BC by investigating histopathological images. The work dealt with both benign and malignant subtypes. Further, the benign cancer subtypes include fibroadenoma, tubular adenoma, phyllodes tumour, and adenosis. Whereas the malignant subtypes contain ductal carcinoma, papillary carcinoma, lobular carcinoma, and mucinous carcinoma.

Social implications of the research

Dr Sudhaker Tummala explains that the computer-aided subtyping of breast cancer from histopathology images using an ensemble of fine-tuned SwinT models can be an alternative to manual diagnoses, thereby reducing the burden on clinical pathologists.

Collaborations

1. Prof. Seifedine Kadry, Department of Applied Data Science, Noroff University College, Kristiansand, Norway
2. Dr Jungeun Kim, Division of Computer Science, Department of Software, Kongju National University, Korea

In the future, Dr Tummala will advance his research to add explainability to the ensemble model predictions and also to develop models that can work on fewer data samples.

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Nanocomposites are the heterogeneous materials that are produced by the mixtures of polymers with inorganic solids that are multi-phased with two or three dimensions of less than 100 nanometers (nm). Nanocomposites offer advanced technologies in enhancing several industrial sectors like automobile, construction, electronics and electrical, food packaging, and technology transfer, yet its sustainable and environment-friendly nature provides a great deal for mankind. Dr Imran Uddin, Post Doctoral fellow, Department of Physics, has published a paper titled “Bioinspired GO/Au nanocomposite synthesis: Characteristics and use as a high-performance dielectric material in nanoelectronics” in the South African Journal of Botany, having an impact factor of 3.11. The paper demonstrated that GO-based materials are better constituents for nanocomposite synthesis and facilitate in enhancing the performance of electrical devices and energy storage systems.

Abstract

A bioinspired method was used to synthesise a graphene oxide (GO) based noble metal (Au) nanocomposite (GO/Au nanocomposite) using chemically exfoliated graphene oxide as the base matrix and gold (Au) nanoparticles. GO’s structural properties and morphology and the GO/Au nanocomposite were determined using XRD, TEM, SEM, EDAX, FTIR, and TGA analysis. LCR analysis was used to characterise the electrical characteristics of GO dielectric features as a function of frequency. The dielectric permittivity and electrical conductivity of GO were very frequency-driven. The results demonstrated that GO has direct current and Correlated Barrier Hopping conductivity processes in the low and high-frequency bands. The dielectric constant of the GO/Au nanocomposite shows that the bioinspired approach includes organic macromolecules capable of modest GO reduction and so modifying the C/O ratio, resulting in an enhancement in the matrix’s dielectric characteristics. This work shows that GO-based materials can be used to scale up high-performance electronic devices, as well as electrical and energy storage systems.

Explanation of the research in layperson’s terms

Energy consumption has increased multifold over the past few years. With increased consumption, the need for energy production and storage has become a pressing priority in the current generation. Dr Imran Uddin’s work aims to propose an idea to synthesise a mixture of two energy-storing materials (gold and carbon) at room temperature. Keeping in view the mentioned aim, he has used plant seeds to create this energy-storing mixture, also known as dielectric material in scientific terms. Through various analyses, he has noticed that this material is able to store electric energy at a lower frequency than the parent material. The superiority of this material comes into play in that when it expires, it can be easily disposed of without creating pollution, which goes hand in hand with the ultimate aim to develop sustainable energy-storing devices.

Dr Imran Uddin has mentioned the practical implication of the groundbreaking research. Capacitors are electronic devices that store electric energy in the form of charges. When a capacitor is linked to a charging circuit, it can store electric energy and release that stored energy when attached to an external circuit (like cars, fans, nuclear weapons, etc.), allowing it to be used as a temporary battery. Moreover, the synthetic GO/Au nanocomposite has the potential to be used as a capacitor material in biomedical applications (defibrillators, blood gas analyzers, pacemakers, biomedicines, etc.), as well as other fields where non-toxicity is essential.

The future prospects of Dr Imran Uddin’s research view an ambitious plan to manufacture more materials at room temperature using the green synthesis root. He also intends to investigate the electrochemical characteristics of environmentally benign materials in the field of electrochemical energy storage, such as supercapacitors and batteries.

Collaborations

University of Pannonia, Hungary

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With the recent advancements in modern wireless body area network (WBAN) communication, the demand for compact low-profile wireless computing devices has witnessed a vast increase. Consequently, the antennas which play a critical role in this network are developed with different polarization in distinct frequency bands so as to maintain better reliability of communication links. Dr Divya Chaturvedi, Assistant Professor, Department of Electronics and Communication Engineering, has published a paper titled, “A Dual-Band Dual-Polarized SIW Cavity-Backed Antenna-Duplexer for Off-body Communication” as first author in the Q1 Journal AEJ – Alexandria Engineering Journal having an impact factor of 6.77. The paper discusses the self-duplexing antennas, offering two channels for concurrent transmission and reception, leading to a simple and compact transceiver.

Abstract

A novel dual-band, dual-polarized antenna-duplexer scheme is intended to be used for WLAN 802.11a and ISM band applications using Substrate Integrated Waveguide (SIW) Technology. The antenna consists of two planar SIW cavities of different dimensions where a smaller sized diamond- shaped cavity is inserted inside the larger rectangular cavity to share the common aperture area. The diamond-ring shaped slots are etched in each cavity for radiation. The larger diamond ring slot is excited with a microstrip feedline to operate at 5.2 GHz while the smaller slot is excited with a coaxial probe to operate at 5.8 GHz. The antenna produces linear polarization at 5.2 GHz (5.1–5.3 GHz) due to the merging of TE 110 and TE 120 cavity modes while circular polarization around 5.8 GHz due to orthogonally excited TM100 and TM010 modes (5.68–5.95 GHz). The slots are excited in an orthogonal fashion to maintain a better decoupling between the ports (i.e. –23 dB). The performance of the antenna has been verified in free space as well as in the vicinity of the human body. The antenna offers the gain of 6.2 dBi /6.6 dBi in free space and 5.8 dBi / 6.4 dBi on-body at lower-/ higher frequency-bands, respectively. Also, the specific absorption rate (SAR) is obtained < 0.245 W/Kg for 0.5 W input power averaged over 10 mW/g mass of the tissue. The proposed design is a low-profile, compact single-layered design, which is a suitable option for off-body communication.

Explanation of the research in layperson’s terms

• This antenna can operate in dual radio frequency bands at 5.2 GHz and 5.8 GHz respectively.
• The antenna can be used in the medical instrument to make it wire-free.
• The antenna is compact in size, thus can be accommodated in a small space.
• The antenna can operate simultaneously at both the frequency bands, thus at the same time it can help in forming links with another on-body antenna and makes the link with Wi-Fi.
• The antenna is validated in terms of Specific absorption rate, hence it is safe to use on the human body.

The paper further expounds on the social implication of this innovative research. Dr Chaturvedi explains that the antenna, being dual-band and dual-polarized, can function as a transceiver circuit. Due to different polarization, it can operate in both the frequency bands simultaneously without affecting the performance. In the first frequency band at 5.2 GHz, it can link with Wi-Fi and in the second frequency band at 5.8 GHz, it is able to communicate with antennas placed in other medical instruments which are used in the vicinity of the human body.

Collaborations

1. Dr Arvind Kumar, Assis. Professor, b Department of Electronics and Communication
Engineering, VNIT Nagpur, India

2. Dr Ayman A Althuwayb, Department of Electrical Engineering, College of Engineering,
Jouf University, Sakaka, Aljouf 72388, Saudi Arabia

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Primary brain tumours make up less than 2% of cancers and statistically occur in around 250,000 people a year globally. Medical resonance imaging (MRI) plays a pivotal role in the diagnosis of brain tumours and advanced imaging techniques can precisely detect brain tumours. On this note, Dr Sudhakar Tummala, Assistant Professor, Department of Electronics and Computer Engineering, has published a paper titled, “Classification of Brain Tumour from Magnetic Resonance Imaging using Vision Transformers Ensembling” in the journal Current Oncology having an impact factor of 3.1. The paper highlights the pioneering breakthrough made in the development of vision transformers (ViT) in enhancing MRI for efficient classification of brain tumours, thus reducing the burden on radiologists.

Abstract of the paper

The automated classification of brain tumours plays an important role in supporting radiologists in decision making. Recently, vision transformer (ViT)-based deep neural network architectures have gained attention in the computer vision research domain owing to the tremendous success of transformer models in natural language processing. Hence, in this study, the ability of an ensemble of standard ViT models for the diagnosis of brain tumours from T1-weighted (T1w) magnetic resonance imaging (MRI) is investigated. Pretrained and fine tuned ViT models (B/16, B/32, L/16, and L/32) on ImageNet were adopted for the classification task. A brain tumour dataset from figshare, consisting of 3064 T1w contrast-enhanced (CE) MRI slices with meningiomas, gliomas, and pituitary tumours, was used for the cross-validation and testing of the ensemble ViT model’s ability to perform a three-class classification task. The best individual model was L/32, with an overall test accuracy of 98.2% at 384 × 384 resolution. The ensemble of all four ViT models demonstrated an overall testing accuracy of 98.7% at the same resolution, outperforming individual model’s ability at both resolutions and their ensemble at 224 × 224 resolution. In conclusion, an ensemble of ViT models could be deployed for the computer-aided diagnosis of brain tumours based on T1w CE MRI, leading to radiologist relief.

A brief summary of the research in layperson’s terms

Brain tumours (BTs) are characterised by the abnormal growth of neural and glial cells. BTs causes several medical conditions, including the loss of sensation, hearing and vision problems, headaches, nausea, and seizures. There exist several types of brain tumours, and the most prevalent cases include meningiomas (originate from the membrane surrounding the brain), which are non-cancerous; gliomas (start from glial cells and the spinal cord); and glioblastomas (grow from the brain), which are cancerous. Sometimes, cancer can spread from other parts of the body, which is called brain metastasis. A pituitary tumour is another type of brain tumour that develops in the pituitary gland in the brain, and this gland primarily regulates other glands in the body. Magnetic resonance imaging (MRI) is a versatile imaging method that enables one to noninvasively visualise inside the body, and is in extensive use in the field of neuroimaging.

There exist several structural MRI protocols to visualise inside the brain, but the prime modalities include T1-weighted (T1w), T2-weighted, and T1w contrast-enhanced (CE) MRI. BTs appear with altered pixel intensity contrasts in structural MRI images compared with neighbouring normal tissues, enabling clinical radiologists to diagnose them. Several previous studies have attempted to automatically classify brain tumours using MRI images, starting with traditional machine learning classifiers, such as support vector machines (SVMs), k-nearest-neighbour (kNN), and Random Forest, from hand-crafted features of MRI slices. With the rise of convolutional neural network (CNN) deep learning model architectures since 2012, in addition to emerging advanced computational resources, such as GPUs and TPUs, during the past decade, several methods have been proposed for the classification of brain tumours based on the finetuning of the existing state-of-the-art CNN models, such as AlexNet, VGG16, ResNets, Inception, DenseNets, and Xception, which had already been found to be successful for various computer vision tasks.

Despite the tremendous success of CNNs, they generally have inductive biases, i.e., the translation equivariance of the local receptive field. Due to these inductive biases, CNN models have issues when learning long-range information; moreover, data augmentation is generally required for CNNs to improve their performance due to their dependency on local pixel variations during learning.Therefore, in this work, the ability of pretrained and fine tuned ViT models, both individually and in an ensemble manner, is evaluated for the classification of meningiomas, gliomas, and pituitary tumours from T1w CE MRI at both 224 × 224 and 384 × 384 image resolutions.

Dr Sudhakar Tummala has mentioned the social implications of the research by expounding that the computer-aided diagnosis of brain tumours from T1w CE MRI using an ensemble of fine tuned ViT models can be an alternative to manual diagnoses, thereby reducing the burden on clinical radiologists. He also explains the future prospects of his research, which is to add explainability to the ensemble model predictions and to develop methods for precise contouring of tumour boundaries.

Details of Collaborations

Prof Seifedine Kadry, Department of Applied Data Science, Noroff University College, Kristiansand, Norway.

Dr Syed Ahmad Chan Bukhari, Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John’s University, New York, USA.

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In the last few years, due to the enormous development in communication technology, the sharing, and transmission of information have increased immensely. The information can be transferred in various forms, such as text, audio, video, and images. Mostly, the information or data is transmitted through open channels, which increases the possibility of illegal interception, fabrication, and modification of the original information. Thus, to avoid unauthorised access or alteration of data, the development of secure transmission systems is very important.

The latest research from the Department of Physics evaluates the security strength of an improved optical cryptosystem based on interference. Assistant Prof Dr Ravi Kumar has published a paper, Security analysis on an interference-based optical image encryption scheme, in the Applied Optics journal, with an impact factor of 1.905.

Dr Ravi Kumar’s research is focused on the area of optical information processing and optical metrology. He studies and designs new optical cryptosystems with enhanced security features. For that, he uses various optical aspects and techniques, such as interference, diffractive imaging, polarization, computational imaging, etc. Alongside this, he also works in the area of digital holography and incoherent imaging. In this, he designs and develops new optical systems for imaging applications, such as super-resolution imaging, biomedical imaging, 3D imaging, telescopic applications, object detection, reconstruction, etc.

Explanation of the Research

Optical systems have been studied extensively for image encryption and found to be more reliable and efficient than their digital counterparts, such as parallel processing, capable of processing 2D data, multi-parameters capabilities (i.e., phase, wavelength, polarization, etc.), and can be employed as the security keys. The usage of biometric authentication in daily life, credit cards, fingerprint authentication, email/bank passwords, etc.; all need to be secured. This research can play an important role in designing a sophisticated cryptosystem for future technologies. Moreover, another direction of the research i.e., optical imaging, can be translated to design new low-cost biomedical devices (endoscopes, microscopes, biomedical sensors, etc.) which can have a significant social impact.

In the future, Dr Ravi Kumar will be focusing on the development of a new robust optical cryptosystem and designing new attack algorithms for existing optical encryption techniques. Additionally, he is also designing new optical imaging systems with better signal-to-noise ratios and improved resolution.

Abstract

In this paper, the security strength of an improved optical cryptosystem based on interference has been evaluated. The plaintext was encoded into a phase-only mask (POM) and an amplitude mask (AM). Since the information of the plaintext cannot be recovered directly when one of the masks is released in the decryption process of an improved cryptosystem, it seems that it is free from the silhouette problem. However, researchers found that the random phase mask (RPM) that served as the encryption key is not related to the plaintext; thus, it is possible to recover the RPM firstly using the known-plaintext attack (KPA). Moreover, the POM and the AM generated in the encryption path only contains the phase and amplitude information, respectively; thus, these can be utilised as additional constraints in the proposed iterative process. Based on these findings, researchers have demonstrated two new kinds of hybrid attacks to crack the cryptosystem, i.e., a KPA and an iterative process with different constraints. To the best of our knowledge, it was the first time that the existence of a silhouette problem in the cryptosystem under study had been reported. Researchers have validated their attacks through numerical simulation.

Collaborations

Dr Xiong Yi, Jiangnan University, Wuxi 214122, China

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Intelligent transport systems embedded with latest technology is the future of automotive industry and can revolutionise the very infrastructure and architecture of transportation. The principles of intelligent transportation systems have manifested in the development of Internet of vehicles (IoV), a novel technological network that enables autonomous and connected mobile commutation facility. Dr Sriramulu Bojjagani, Assistant Professor, Department of Computer Science and Engineering, has published a paper on this seminal topic titled “Secure Authentication and Key Management Protocol for Deployment of Internet of Vehicles (IoV) Concerning Intelligent Transport Systems” in the prestigious journal, IEEE Transactions on Intelligent Transport Systems (Q1 journal) having an Impact factor of 9.551. The paper highlights the development and significance of the pioneering AKAP-IoV system in successfully tackling cyber threats and security vulnerabilities.

Abstract

Intelligent transport systems amalgamated with advanced technologies are an important element of the automotive industry, including critical infrastructure and transportation. Internet of Vehicles (IoV) is the modern technological framework designed for intelligent transportation. IoV creates a network of information relations among vehicles, thus contributing to reduced congestion, roadside infrastructure, driver/traveller safety, and traffic efficiency through wireless communication and sensing technology. However, a significant challenge in IoV applications is security, as criminals could potentially exploit these applications. Despite increasing industry awareness, the potential danger posed by security vulnerabilities and cyber threats remains high. In this study, we have designed a new system called AKAP-IoV, which supports secure communication, mutual authentication, and key management among vehicles, roadside units, and fog and cloud servers. AKAP-IoV was tested and verified using Scyther and Tamarin to ensure its resistance to cyber threats. Furthermore, we conducted a formal security analysis using the Real-or-Random (RoR) oracle model to assess security properties logically. In addition, a detailed, comprehensive comparative study was considered to evaluate the performance, functionality, efficiency, and security features supported by AKAP-IoV compared to those of recently developed schemes.

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Tropical forests are the breathing lungs of the world that work as a collective to stabilise climate by absorbing vast amounts of carbon dioxide. Covering over 12% of Earth’s total land surface. It functions as a harbour for evolution and supplying prerequisite rainfall for healthy vegetation. Recent global climate changes have catalysed drastic effects on the tropical forests and the healthy functioning of the same. A critical understanding of the present state of forest ecosystems is crucial for undertaking necessary measures to prevent further degradation.

Recently, the project entitled Assessment and modelling of carbon and nitrogen dynamics in tropical forests of Eastern Ghats, Andhra Pradesh in response to climate change, has been sanctioned to Dr Javid Ahmad D, Assistant Professor, Department of Environmental Science by DST – SERB (Science and Engineering Research Board), Govt of India, with a total outlay of Rs. 28.54 lakhs. Dr Javid Ahmad D is the principal investigator of the project.

A complete dataset on long-term dynamics in vegetation, the C and N generated for different tropical forest types in the Eastern Ghats landscape would serve as a baseline data for forest managers and policy-makers. The project aims to gain insight on the response of these tropical forests to changing global climate in relation to multiple factors (elevated CO2, increased temperatures and altered precipitation).

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The Department of Electronics and Communication Engineering is glad to announce that Mr Garikapati Anith Chowdary, a BTech passed-out student has published a paper in collaboration with Assistant Professor Dr M Durga Prakash. The paper titled Tunnel Field Effect Transistor Design and Analysis for Biosensing Applications was published in the Q2 journal Silicon having an Impact Factor 2.941.

The physical modelling of the tunnel field effect transistor (TFET) is done in this study. The Silvaco TCAD tool is used to design and simulate the TFET structure. The FET device has attracted a lot of attention as the ideal tool for creating biosensors because of its appealing properties such as ultra-sensitivity, selectivity, low cost, and real-time detection capabilities in a sensing point of view.

These devices have a lot of potential as a platform for detecting biomolecules. Short channel effects, specificity, and nano-cavity filling have all been improved in FET-based biosensors. FET-based biosensors are appropriate for label-free applications. Random dopant variations and a thermal budget are seen during the construction of a JLFET. To overcome this problem, the charge-plasma-based concept was established in FETs in this study.

Different metallurgical functions for electrodes were employed in this biosensor to behave as a p-type source and n-type drain. To alleviate the short channel effects, a dual material gate work function for the gate electrode was devised, as well as a double gate architecture. Biomolecules can be neutral or charge-based, and both types of biomolecules can be identified using a proof-of-concept FET-based biosensor. Changes in the drain current (Id) of the device were achieved by varying dielectric values and charges in the cavity region with variable cavity lengths.

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