Clinical Decision Support System for Unani Medicine Practitioners

Like other fields of Traditional Medicines, Unani Medicines have been found as an effective medical practice for ages. It is still widely used in the subcontinent, particularly in Pakistan and India. However, Unani Medicines Practitioners are lacking modern IT applications in their everyday clinical practices. An Online Clinical Decision Support System may address this challenge to assist apprentice Unani Medicines practitioners in their diagnostic processes. The proposed system provides a web-based interface to enter the patient's symptoms, which are then automatically analyzed by our system to generate a list of probable diseases. The system allows practitioners to choose the most likely disease and inform patients about the associated treatment options remotely. The system consists of three modules: an Online Clinical Decision Support System, an Artificial Intelligence Inference Engine, and a comprehensive Unani Medicines Database. The system employs advanced AI techniques such as Decision Trees, Deep Learning, and Natural Language Processing. For system development, the project team used a technology stack that includes React, FastAPI, and MySQL. Data and functionality of the application is exposed using APIs for integration and extension with similar domain applications. The novelty of the project is that it addresses the challenge of diagnosing diseases accurately and efficiently in the context of Unani Medicines principles. By leveraging the power of technology, the proposed Clinical Decision Support System has the potential to ease access to healthcare services and information, reduce cost, boost practitioner and patient satisfaction, improve speed and accuracy of the diagnostic process, and provide effective treatments remotely. The application will be useful for Unani Medicines Practitioners, Patients, Government Drug Regulators, Software Developers, and Medical Researchers.Comment: 59 pages, 11 figures, Computer Science Bachelor's Thesis on use of Artificial Intelligence in Clinical Decision Support System for Unani Medicine

A blockchain-based deep-learning-driven architecture for quality routing in wireless sensor networks

Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities. © 2013 IEEE

Exploring Plant Genetic Variations with Morphometric and Molecular Markers

For centuries, crop improvement has served as the basis of food security of ever increasing human population. Though vast germplasm collections are available; their exploitation for crop improvement still depends upon efficient assessment of genetic diversity. Genetic variability is the key element in adaptation of plants to varying climates. While crops with narrow genetic diversity are vulnerable to stresses. The estimation of extent and pattern of genetic variability is a prerequisite for generating superior varieties. Genetic diversity analysis generates key information to dissect genetic variations in crop germplasm with the help of morphometrical, biochemical and molecular tools. Among these, DNA markers provide a reliable and detailed insight into the similarities and differences among crops. In this chapter, we discuss the applications of phenotypic and molecular markers to probe genetic divergence in crops and present case studies that describe the significance of these tools to characterize sorghum germplasm. Furthermore, we spotlight sorghum biodiversity exploration efforts worldwide and propose future directions

Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs

Underwater wireless sensor networks (UWSNs) facilitate a wide range of aquatic applications in various domains. However, the harsh underwater environment poses challenges like low bandwidth, long propagation delay, high bit error rate, high deployment cost, irregular topological structure, etc. Node mobility and the uneven distribution of sensor nodes create void holes in UWSNs. Void hole creation has become a critical issue in UWSNs, as it severely affects the network performance. Avoiding void hole creation benefits better coverage over an area, less energy consumption in the network and high throughput. For this purpose, minimization of void hole probability particularly in local sparse regions is focused on in this paper. The two-hop adaptive hop by hop vector-based forwarding (2hop-AHH-VBF) protocol aims to avoid the void hole with the help of two-hop neighbor node information. The other protocol, quality forwarding adaptive hop by hop vector-based forwarding (QF-AHH-VBF), selects an optimal forwarder based on the composite priority function. QF-AHH-VBF improves network good-put because of optimal forwarder selection. QF-AHH-VBF aims to reduce void hole probability by optimally selecting next hop forwarders. To attain better network performance, mathematical problem formulation based on linear programming is performed. Simulation results show that by opting these mechanisms, significant reduction in end-to-end delay and better throughput are achieved in the network

Efficient nanostructured materials to reduce nutrient leaching to overcome environmental contaminants

Abstract Nutrient leaching is a major reason for fresh and ground water contamination. Menthol is the major bioactive ingredient of Mentha arvensis L. and one of the most traded products of global essential oil market. The indigenous production of menthol crystals in developing countries of the world can prove to be the backbone for local growers and poor farmers. Therefore, present research was designed to check the effects of nano-structured plant growth regulators (PGRs) (28-homobrassinolide and ethephon) with reduced leaching potentials on the essential oil and menthol (%) of Mentha arvensis L. The prepared nano-formulations were characterized by Fourier transform infrared (FTIR) spectroscopy, Laser induced breakdown spectroscopy (LIBS), Differential scanning colorimetry-thermal gravimetric analysis (DSC-TGA), Scanning electron microscopy (SEM), Atomic absorption spectrometry (AAS) and Zeta potential and Zeta size analysis. The menthol (%) was determined by modified spectrophotometric and gas chromatographic (GC) method. The highest essential oil (%) was obtained by the application of 28-homobrassinolide-Zn-NPs-L-II (0.92 ± 0.09%) and ethephon-Ca-NPs-L-III (0.91 ± 0.05%) as compared to the control (0.65 ± 0.03%) and blank (0.62 ± 0.09%). The highest menthol (%) was obtained by applying 28-homobrassinolide-Ca-NPs-L-I (80.06 ± 0.07%), 28-homobrassinolide-Ca-NPs-L-II (80.48 ± 0.09%) and 28-homobrassinolide-Ca-NPs-L-III (80.84 ± 0.11%) and ethephon-Ca-NPs-L-III (81.53 ± 0.17%) and ethephon-Zn-NPs-L-II (81.93 ± 0.26%) as compared to control (67.19 ± 0.14%) and blank (63.93 ± 0.17%)

Mobile Sinks Assisted Geographic and Opportunistic Routing Based Interference Avoidance for Underwater Wireless Sensor Network

The distinctive features of acoustic communication channel-like high propagation delay, multi-path fading, quick attenuation of acoustic signal, etc. limit the utilization of underwater wireless sensor networks (UWSNs). The immutable selection of forwarder node leads to dramatic death of node resulting in imbalanced energy depletion and void hole creation. To reduce the probability of void occurrence and imbalance energy dissipation, in this paper, we propose mobility assisted geo-opportunistic routing paradigm based on interference avoidance for UWSNs. The network volume is divided into logical small cubes to reduce the interference and to make more informed routing decisions for efficient energy consumption. Additionally, an optimal number of forwarder nodes is elected from each cube based on its proximity with respect to the destination to avoid void occurrence. Moreover, the data packets are recovered from void regions with the help of mobile sinks which also reduce the data traffic on intermediate nodes. Extensive simulations are performed to verify that our proposed work maximizes the network lifetime and packet delivery ratio

The novel approach of catalytic interesterification, hydrolysis and transesterification of Pongamia pinnata oil

The properties of biodiesel are completely dependent on the fatty acid profile of feedstock oils. Several feedstocks are not in use for biodiesel production because of the presence of unsuitable fatty acids in their oils. The present study was conducted to overcome this problem by the utilization of interesterification and hydrolysis processes. The present study reports biodiesel with much better cold flow properties than previous studies. Fatty acids present in Pongamia pinnata oil were optimized via interesterification and hydrolysis treatment of feedstock prior to alkali-catalyzed transesterification. The physiochemical properties of fuel were evaluated by standard test methods and the results were compared with EN 14214 and ASTM D6751 standards. Biodiesel composition was analyzed by a gas chromatographic analysis. The density, saponification and iodine values of the biodiesel derived from treated and non-treated oil were found to be within the range recommended by the international fuel standards. The acid values of biodiesel produced from non-treated and treated fractions were high (0.7–0.8 mg of KOH/g of oil), as compared to the biodiesel produced from non-treated and treated pure oil. The cloud points and pour points of biodiesel produced from hydrolyzed and interesterified oil were in the range of (8.1 to −9.6 °C) and (2.03 to −12.5 °C), respectively, while those of non-treated oil were in the range of (13.37 to −1.53 °C). These results indicate that treatments of oil specifically improved the low-temperature properties of biodiesel

Enhancing functionalities in nanocomposites for effective dye removal from wastewater: isothermal, kinetic and thermodynamic aspects

The adsorption process combined with electrocoagulation is a highly effective technique for dye removal. Calcinized and non-calcinized composites based on bentonite and sodium zeolite were prepared for adsorptive removal of Foron EBL blue, Terasil brown 2RFL, Torque blue PG, and Orange P3R dyes. Factors affecting the adsorption process, such as contact time, initial dye concentration, and temperature, were also explored in this study. Equilibrium data of natural clay composites was explained by Freundlich, Langmuir, Dubinin Radushkevich isotherm, Harkin Jura, and Temkin isothermal models. Harkin Jura isotherm model best fitted on the adsorption mechanism compared to Langmuir and Temkin isotherm model. Morphology of clay-based adsorbents and functional group arrangement were investigated by scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR). The calcinized nano-composite material exhibited better adsorption capacity than non-calcinized nano-composite and could be employed as a low-cost alternative for dye removal

Enhancing Functionalities in Nanocomposites for Effective Dye Removal from Wastewater: Isothermal, Kinetic and Thermodynamic Aspects

The adsorption process combined with electrocoagulation is a highly effective technique for dye removal. Calcinized and non-calcinized composites based on bentonite and sodium zeolite were prepared for adsorptive removal of Foron EBL blue, Terasil brown 2RFL, Torque blue PG, and Orange P3R dyes. Factors affecting the adsorption process, such as contact time, initial dye concentration, and temperature, were also explored in this study. Equilibrium data of natural clay composites was explained by Freundlich, Langmuir, Dubinin Radushkevich isotherm, Harkin Jura, and Temkin isothermal models. Harkin Jura isotherm model best fitted on the adsorption mechanism compared to Langmuir and Temkin isotherm model. Morphology of clay-based adsorbents and functional group arrangement were investigated by scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR). The calcinized nano-composite material exhibited better adsorption capacity than non-calcinized nano-composite and could be employed as a low-cost alternative for dye removal

The Novel Approach of Catalytic Interesterification, Hydrolysis and Transesterification of Pongamia pinnata Oil

The properties of biodiesel are completely dependent on the fatty acid profile of feedstock oils. Several feedstocks are not in use for biodiesel production because of the presence of unsuitable fatty acids in their oils. The present study was conducted to overcome this problem by the utilization of interesterification and hydrolysis processes. The present study reports biodiesel with much better cold flow properties than previous studies. Fatty acids present in Pongamia pinnata oil were optimized via interesterification and hydrolysis treatment of feedstock prior to alkali-catalyzed transesterification. The physiochemical properties of fuel were evaluated by standard test methods and the results were compared with EN 14214 and ASTM D6751 standards. Biodiesel composition was analyzed by a gas chromatographic analysis. The density, saponification and iodine values of the biodiesel derived from treated and non-treated oil were found to be within the range recommended by the international fuel standards. The acid values of biodiesel produced from non-treated and treated fractions were high (0.7–0.8 mg of KOH/g of oil), as compared to the biodiesel produced from non-treated and treated pure oil. The cloud points and pour points of biodiesel produced from hydrolyzed and interesterified oil were in the range of (8.1 to −9.6 °C) and (2.03 to −12.5 °C), respectively, while those of non-treated oil were in the range of (13.37 to −1.53 °C). These results indicate that treatments of oil specifically improved the low-temperature properties of biodiesel