Journal of Integrated Science and Technology
The Journal of Integrated Science and Technology is a peer reviewed journal for publication of fundamental and applied research from all disciplines of science (Chemistry, Physics, Biology, Mathematics, environmental science) and technology (engineering fields and technology advances). The Journal has moved to new platform (OJS 3) from 2023, contents of the journal (2023 onwards) are available on https://pubs.thesciencein.org/journal/index.php/jist (new site). Authors need to submit their article as per instructions provided on new plateform.
Optimizing the performance of Triple Input DC-DC converter in an Integrated System
Power electronic converters are absolutely necessary for the notion of a hybrid energy system to integrate hybrid energy sources (HES). In this paper, a triple input DC-DC converter with buck-boost operating ability is presented. Multiple energy sources with various I-V characteristics can be integrated using the converter. Using the voltage-second concept, the output equation of the converter is determined from various operational conditions. The MATLAB / SIMULINK software was used to develop and simulate this converter topology. For the purpose of assessing the performance of the converter three different input sources with various voltage levels of 40 V, 30 V, and 16 V are used. Additionally, the adeptness profile of the proposed converter under different loading conditions has been examined, and this analysis demonstrates clearly that the converter under discussion has a high level of efficiency.
Evaluation of Z-Source Inverter Topologies for Power Conditioning Unit for DC Power Supply Systems
In order to operate a wind turbine generator, careful and extensive research is required as different environmental conditions result in different wind speeds. Standard type of induction generators and compatible electric generators are great in the construction of low-speed applications. Due to the improved architecture and lower PM material emissions, a permanent magnet generator is an ideal choice for low-speed direct applications. Different topologies of permanent magnet generator are available, such as radial flux, axial flux, and transverse flux PMG. The sophisticated structure of the permanent axial flux generator is not suitable for use in large wind turbines. Due to the simple structure of radial flux PMG, the formation of multiple poles can be easily incorporated into the nascalle of wind turbines. To overcome these obstacles, this research project proposes a new topology quasi-Z-source Matrix converter-based DC / DC using a zigzag transformer, and simulation is performed and compared with other existing DC-based Z-source converters. DC will be approved at the beginning of the power supply unit in DC power supply systems.
Single Point Incremental Forming (SPIF) of Aluminum Alloy AA 1050: Experimental evaluation of the effect of process parameters on surface roughness
The work on single point incremental forming of Aluminium alloy (AA1050) was done experimentally. The impact of process variables including tool shape, tool size, step size, and feed was examined on surface roughness. Research was conducted utilizing the Response Surface Methodology (RSM). The relevance of process parameters was determined using analysis of variance (ANOVA). The tool's form was found to have the biggest impact on surface roughness. The surface roughness is also greatly influenced by the tool size and step size. Higher feed rates can be used without degrading surface quality since feed has the least impact on surface roughness.
Comparative assessment of anti-anemic effect of Sucrosomial iron in haloperidol-induced iron deficiency anemia in Wistar rats
Anemia affects almost one-third of the world's population. Conventional oral iron salts have limitations such as poor bioavailability and poor tolerability. Sucrosomial iron gets absorbed through the hepcidin independent pathway; hence overcoming all these limitations. The present study assessed the anti-anemic effect of various iron salts on hematological parameters in haloperidol-induced iron deficiency anemia in Wistar rats. Iron deficiency anemia was induced by haloperidol injection (2.5 mg/kg body weight/day i.m.) for initial 5 days along with an iron-deficient diet throughout the study. Disease control animals received ferrous sulfate, ferrous ascorbate, ferrous fumarate, and Sucrosomial iron at a dose of 30 mg/kg p.o for 15 days. On day 20, animals treated with all iron-supplemented groups showed significant improvement in hematological parameters. Interestingly, the Sucrosomial iron group showed significantly higher improvement in hemoglobin levels and hematocrit parameters than other oral iron salt groups (P<0.05). This effect may be due to the higher bioavailability of Sucrosomial iron. We concluded that Sucrosomial iron exhibited a potent anti-anemic effect against haloperidol-induced iron deficiency anemia in Wistar rats compared to other conventional oral iron salts.
Design of 5G Microstrip patch array antenna for gain enhancement
A Novel 5G antenna is designed in this research, which resonates at 28GHz. In modern wireless communication, there is a need to develop a 5G antenna with enhanced gain value, and the antenna size should be small and low-profile. The design of the patch of the 5G antenna consists of a rectangular ring of 8mmx5mm. The rectangular ring is of 1mm size from all sides. Various antenna parameters are measured, such as S-parameter [1,1], gain, radiation pattern, and bandwidth. The return loss of the antenna obtained is -24.79dB. The average gain obtained in the proposed 5G antenna is 7.9 dBi, and the radiation pattern of the antenna is Unidirectional, bandwidth range is 27.36-29.29GHz, with centre frequency 28.108GHz, and four-element array antennas are with merits of average gain of 13.385dBi, bandwidth ranges from 27.29-29.14GHz, centre frequency 28.024GHz. A 5G array antenna of the size 1x4 is designed, which greatly increases the antenna's gain. A Defective ground structure (DGS) is implemented on the ground plane to improve the antenna performance. The DGS is implemented on the ground plane by introducing three Concentric circles, and the antenna's bandwidth is improved using the Concentric circles. The array antenna enhances some antenna characteristics. An edge feeding strategy is used to feed the antenna, and the ANSYS HFSS programme simulates the proposed antenna.
An EOQ model for Inventory System dependent upon on hand inventory without shortages
The inventory management in supply chain remain crucial for the proper availability of all goods. The mathematical and computer algorithms are implemented to channelize the distribution and availability of stock at particular time and specific point of chain supply. The cumulative impact of all algorithms and models describes the physical stock availability under different situations arising because of high demand, variable stock, distributed inventory and all other alike factors. In this paper we present a model to study a situation where the demand rate declines along with stock level down. The demand rate is different for different situations i.e, the demand rate is when and when where is the inventory level. Numerical examples and sensitivity analysis are presented to illustrate the model developed.
Multiband frequency switchable Microstrip antenna design for satellite application
This article illustrates a novel PIN diode-based small microstrip multiband reconfigurable antenna design. Three frequencies, 4.85GHz, 10.04GHz, and 15.09GHz, are resonant with the proposed design. These bands cover the roughly equivalent C band (4-8GHz), X band (8-12GHz), and Ku band (12-18GHz) microwave frequency bands. The PIN diodes are then added to this antenna to enable reconfigurability. A novel reconfigurability technique is put forth to address the difficulties associated with multiband operation. In close proximity to the microstrip line, the three pin diodes are sorted to ground with parasitic for each of the three bands. The parasitic element is connected to a particular PIN diode microstrip line that, when turned ON, for single frequency band. The scattering parameters loss plots analyzed with far field radiation pattern along with smith chart.
An empirical research on modified concrete using demolition construction waste as partial substitutes of fine aggregates
Nowadays, Demolition construction waste is in very high demand in the market and is also useful to improve the higher strength of designed ecofriendly concrete compared to Conventional Concrete. Natural sand and fine aggregate supplies are diminishing in the market as a result of large major building and the desire to minimize the cost of concrete production by using demolition construction waste as a substitute of fine aggregates in the modified green concrete. The utilization of demolition construction wastes as fine aggregate in concrete would also be beneficial in economic impact and also maintain the environment. The durability and mechanical properties of modified concrete are improved. Various Grade of mix design of concrete as per (IS 10262:2009) can give better results with demolition construction waste as replacement of fine aggregates at age of 28 days curing periods. From the various literature review, here various results obtained it is suggested that demolition construction waste with a replacement level of up to 20% can be used as a fine aggregate for improvement of modified concrete.
Field Programmable Gate Array (FPGA) based implementation of efficient RC6 block cipher
The efficient encryption system is required to achieve goal of security services. Rivest cipher 6 is a symmetric key block cipher which incorporates data dependent rotations. RC6 is specified as RC6-w/r/b, where the parameters w, r, and b respectively express the word size (in bits), the number of rounds, and the size of the encryption key (in bytes). In current work, optimized RC6 is implemented using xc7vx330t-2-ffg1157 field programmable gate array with proposing of inclusion of RC6-32/20/16. High value of rounds creates more diffusion and enables more security. Proposed system is synthesized and implemented on virtex7 field programmable gate array. The proposed method has less resource utilization and high throughput. Resource utilization in terms of slices is only 1% and in terms of fully used LUT-FF pair is 15%. Throughput of proposed system is 99.22 Gbps and efficiency is 50.596 Mbps/slice. Security analysis by performing avalanche test and strict avalanche criterion is also done. Average Avalanche effect of 54.71 is achieved, which satisfies criteria of SAC.
Discrete Hartley Transform and its applications - A review
Digital signal processing (DSP) has become the single most powerful essential component in all technological applications, which include multimedia as well as mobile communications, data compression, network cameras, mobile phones, sensor imaging, acoustic beam formers, GPS, but also biomedical signal processing, and so on. Almost all of these applications place various demands on DSP systems, such as the ability to handle high throughput data as required by real-world applications, the requirement for reduced power, and the need for chip space. Transforms are helpful to convert one data type to another in DSP applications. The Hartley Transform (DHT) is important in Digital Signal Processing. When the input sequence is real, DHT is equivalent to Fast Fourier transform and it is utilized in the applications of image and optical signal processing, computer vision as well as teleconferencing and processing or analysis of moving images. Many DHT algorithms are developed by the authors for the application of optical signal processing, image processing as well as VLSI implementations and the review has been presented in this paper.
Development of suitable closed loop system for effective wind power control using different ZSC topologies and different switching techniques
The use of wind energy, a significant renewable energy source, has been expanding quickly in recent decades on a global scale. Higher standards for the power output and dependability of generators and converters are necessary due to the rising capacity of both onshore and offshore wind power generation. Multiphase wind power generation systems have clear advantages over traditional three-phase wind power generation systems in low-voltage, high-power operation, improved fault-tolerant, and increased degrees of control freedom, which help them gain more and more traction in the field of contemporary wind power generation. This paper provides an overview of the multiphase energy conversion of wind power generation and introduces relevant technological advancements, such as the multiphase converter topologies, modelling, and control of multiphase generators. This paper will provide an overview of DC-AC and AC-AC ZSCs, as well as the case for their application as wind power converters in a variety of topologies. This work suggests a novel direct torque control space vector modulation (DTC-SVM) based closed loop speed control of an induction motor supplied by a high-performance Z-source inverter (ZSI).
The robust and efficient Machine learning model for smart farming decisions and allied intelligent agriculture decisions
Crop Yield Prediction is essential in today's rapidly changing agricultural market (CYP). Accurate prediction relies on machine learning algorithms and selected features. Any machine learning algorithm's performance might well be enhanced by introducing a diverse set of features into the same training dataset. Crop yield prediction includes parameters such as temperature, humidity, pH, rainfall, as well as crop name in forecasting the yield of the crop based on historical data. It offers us an indication of the best crop to expect in terms of weather conditions in the field. Crop prediction is a difficult task in the agricultural realm. The primary purpose of this research is to offer a novel machine learning approach for a heterogeneous data environment containing IoT-sensed data about the environment, agricultural conditions, plants' features, demands, etc. We have used data set of the following five crops (rice, ragi, gram, potato & onion) collected from Andhra Pradesh, Kaggle repository. In this work, we utilized diverse machine learning as well as deep learning algorithms such as Regression methods, Decision tree, Naive Bayes, SVM, K-Means, Expectation-Maximization (EM), and AI techniques (LSTM, RNN). It seems that among machine learning techniques, the Random Forest algorithm outperforms with 99.27% training accuracy for crop yield prediction. However, among sigmoid, ReLu, and tanh activation, sigmoid achieves 99.71 percent accuracy with four hidden layers for predicting the crop yield prediction.
Niosomal formulation of Quercetin and Resveratrol and in-vitro release studies
Dietary polyphenols from plant origins play a major role in the human diet. They supply efficient antioxidants that reduce or prevent ROS production depending on the concentration. However, these polyphenols are less bio-available in the body due to various parameters, including low intrinsic activity, poor absorption, high metabolism, inactivity of metabolic products, and/or rapid elimination. Quercetin and resveratrol are dietary polyphenols that are often found in the human diet. However, they lag in bioavailability, which makes them less preferred nutraceuticals. This particular study is aimed at increasing the bioavailability of quercetin and resveratrol through the nano vector system, niosomes. In this study, niosomes entrapped with Quercetin and Resveratrol were produced in different concentrations of Span 60 and cholesterol using the thin film hydration method. The best suitable composition, which provides maximum entrapment, was taken for further study. The niosomal formulation of quercetin and resveratrol was evaluated using various methods like solubility and shape. The entrapment efficiency was determined to be 61.55%. The niosomes were then characterized using a zeta sizer and a potential. The average particle size of niosomes was 194 diameter values in nanometers, and their zeta potential was -20 mV, which indicated their good stability. The results of the in vitro drug release research, which was conducted using phosphate buffer saline pH 7.4, were that 92.6% in 24 hours was significantly increased compared to quercetin and resveratrol release, 71.30%. The ex vivo drug release was 94.5% after 24 hours, which was higher when compared to quercetin and resveratrol release of 75.74%. The results of this study indicate that the niosomes significantly enhanced the bioavailability of quercetin and resveratrol.
Review of fundamentals of Artificial Intelligence and application with medical data in healthcare
Artificial Intelligence (AI) can identify substantial interactions by considering the datasets in the emerging era of technologies. It is widely applied in various applications, and healthcare is among them. AI plays a vital role in clinical applications in predicting the disease type, treating the disease, managing chronic situations, and diagnosing the same. AI in the healthcare environment has simplified the lives of doctors, patients, and administrators at hospitals by operating various tasks with lesser computation time and accurate results. The unique challenges such as availability, accessibility, and affordability of AI have contributed to healthcare applications' success. Another factor that enhances the functionality of AI is based on the sourced medical data, which is analyzed, and a model is built to predict the disease by the application of Machine Learning (ML). The other reason for the successful application of AI in the healthcare environment is Computational Intelligence (CI), which is an analysis, design, theory, and development of linguistically and biologically motivated computational techniques. The functionality of CI is identified on the three pillars such as Fuzzy Systems, Neural Networks, and Evolutionary Computation. This research work mainly discusses the various applications of AI in the current healthcare environment. The discussion also includes the different branches of AI with their applications and working principles. Artificial Intelligence and Medical Data
Review of reliability indices of the water distribution system
Over the last few years, there has been a growing emphasis on reliability in water dissemination networks. The water supply network's dependability is crucial in today's water delivery system. The capacity of a water distribution network to fulfil requirements with significant pressure under normal and abnormal situations is referred to as system reliability. The development of a system for analyzing and enhancing the reliability of water delivery systems is underway. Enhanced options are offered to increase network dependability, and then an optimization study is used to choose the best upgrade option based on a predetermined goal function. Reliability does not rely on certain criteria. In today's world, computer-aided programs impact the simulation model and the water supply network study. This analysis shows the factors that can be utilized to determine dependability.
Optimized energy-efficient multi-hop routing algorithm for better coverage in mobile wireless sensor networks
The Mobile Wireless Sensor Network (MWSN) comprises transceivers that collect information and transfer it to the access point through other hubs. Both mobile networks and access points can be portable and work apace with stable devices in the network, depending on the application requirements. Numerous studies have been undertaken to develop sensor nodes considering energy and mobility, with LEACH-based routing algorithms providing the best results. However, the Low Energy Adaptive Clustering Hierarchy-based energy-efficient navigation system best suits small-scale systems. Whenever the connection is huge, long-distance transmission between cluster members and the Base Station (BS) consumes much energy. Thus to overcome it, this research grants an innovative scheduling algorithm that adapts to sensor node transmission to deliver dependable and energy-efficient navigation named Optimized Energy Efficient Routing Algorithm for Better Coverage in Wireless Sensor Networks. Firstly, our research paper presents a unique method called Low Energy Adaptive Tier Clustering Hierarchy (LEATCH), which achieves a reasonable balance of latency and energy usage while addressing various covering concerns in MWSN. Moreover, a novel Honey Bee Algorithm is utilized to find the potential and hyper-cluster heads. As a result, based on power, latency, coverage, stability period, and scalability, the optimized LEATCH protocol outperforms other energy-efficient clustering protocols.
A novel attribute based access control model with application in IaaS cloud
Cloud computing is viewed as one of the most dominant ideal models in the Information Technology industry nowadays. It offers new savvy administrations on-request like Software as a Service, Infrastructure as a Service, and Platform as a Service. Nonetheless, with these administrations promising offices and advantages, there are yet various difficulties related to using cloud computing, for example, data security, maltreatment of cloud administrations, malicious insiders, and cyber-attacks. Among all security necessities of cloud computing, access control is one of the fundamental prerequisites to keep away from unapproved access to frameworks and safeguard association's resources. Albeit different access control models and policies have been grown for various conditions, these models may not satisfy the cloud's access control necessities. It used a portion of the PM's parts alongside a proof-of-idea execution to implement ABAC augmentation for OpenStack while keeping OpenStack's present RBAC design set up. This gives the advantages of upgrading access control flexibility with help of client attributes while limiting the upward of changing the current OpenStack access control structure. The use cases are presented to portray added advantages of the proposed model and show authorization results.
Effects of blending bioethanol with gasoline on spark-ignition engine - A review
Due to strict government regulations and fossil fuel depletion, it was necessary to look for alternatives to traditional fuel sources. Energy demand is increasing day by day due to improved transportation and population growth. Biofuel is an alternative fuel derived from various types of biomasses. Biofuels are receiving scientific and public attention. This can be caused by factors such as the need to strengthen energy security, rising oil prices, and concerns about greenhouse gas (GHG) emissions from fossil fuels. Biofuels are especially attractive to developing countries because they can arouse economic development in rural areas and alleviate poverty by creating employment opportunities & higher incomes in the agriculture sector. Â To evaluate the effects of diesel/ gasoline on engine operation much research had been carried out. The blending of bioethanol leads to improve physicochemical properties which are responsible to improve the SI engine. SI engine operation improves with the help of Physico-chemical properties of bioethanol. The present review illustrates some of the recent research findings on the production of bioethanol from different types of biomasses, their physicochemical properties, and their impact on the engine: combustion characteristics, engine performance, emissions, the effect of bioethanol gasoline blending & operating condition for NOx emission in SI engine.
Automated hybrid Deep Neural Network model for fake news identification and classification in social networks
The rapid growth of social media has far-reaching impacts on civilization, traditions, and economics, including both beneficial and unfavourable implications. Since social networking sites have become more frequently utilized for transmitting data, they have also become a gateway for the distribution of fake news for diverse financial and legislative goals. Artificial Intelligence (AI) and Natural Language Processing (NLP) approaches have a lot of ability for academics who wish to design models that can recognize fake news automatically. On the other hand, identifying fake news is a difficult issue because it demands systems that describe the news and then contrast it to the actual news to categorize it as fake. Thus, to overcome this, this paper introduces Hybrid Deep Neural Network Model, in which C-DSSM and Deep CNN models have been utilized. It identifies and classifies fake news using the LIAR dataset. According to experimental results, the proposed model obtained an accuracy of 92.60%, a recall of 92.40%, a precision of 92.50%, and an F1 score of 92.50%. Furthermore, the proposed model is compared to earlier studies for fake news identification using the LIAR dataset, and the proposed model's performance is remarkable. As a result, the proposed hybrid model gives better results in detecting and classifying fake news on social networks.
Numerical study due to mixed convection nanofluid flow with the effect of velocity slip and thermal conductivity across curved stretching surface
The current research investigates mixed convection across curved stretching surface. This analysis takes into account the effect of velocity slip as well as thermal conductivity. The boundary layer problem is expressed as a mathematical system of equations. Equations in a non-dimensional form are derived by applying an appropriate similarity transformation. Matlab is employed to compute the numerical solutions of the highly nonlinear system of ordinary differential equations. For various values of relevant parameters, substantial variations in the velocity, temperature, and concentration profiles were found. Graphs and tables are used to illustrate the results. It has been shown that due to the rising value of curvature parameter the skin friction coefficient drops.
Framework, semantic and standard approaches in multi-clouds to achieve interoperability: A survey
Cloud computing being the latest powerful way of storing data and hiring services on a server with no burden of hardware procurement. The facility to access data from the comfort of office without having the server physically has raised huge interest in the industry. Number of new cloud service providers has emerged with their own agreements and business models. Over a period of time clients have showed a proclivity towards switching cloud service providers for various reasons ranging from cost, efficiency, nature of business, operability, services, uptime and so on. There are clients who wish to draw more benefit from having multi cloud operations again due to various business reasons. This leads to need of amalgamation of clouds, interfaces between clouds, Application Programming Interfaces (API), collaboration of services and so on. This paper presents the existing different approaches so far which are popular along with the need for further research with respect to semantics, stndard and framework of cloud for interoperability. There are however semantic, standard and Framework endeavors are deficient. The objective of the research is to feature the difficulties changes needed in the semantic of cloud for operating on multi clouds distinguishing semantic, standard and Framework activity that would be expected for relocating and coordinating services in the multi-cloud environment sooner rather than later.
Association of gender, age, and comorbidities with COVID-19 infection in India
Due to a lack of data on various parameters with COVID-19 in the Indian population, this study was carried out to understand the relation among gender, age and comorbidities in Indian population. The data was collected using a questionnaire-based survey form that included questions on demographic characteristics, infection and any pre-underlying conditions (n=1146). The data showed that the male patients had suffered more from COVID-19 (58.6%). Also, the patients suffering from comorbidity are more likely to suffer from a severe form of COVID-19 and obesity/overweight was identified as the most prevalent (n=69) comorbid condition, followed by diabetes (n=35), thyroid (n=19) and hypertension (n=11). In severe COVID-19 cases, 85% of patients had a comorbid condition. In another study of COVID-19 hospitalized-cases, about 97% of patients were found to have an underlying medical condition. Among these, diabetes (55.9%) was identified as the most prevalent comorbidity. Males and older people are at a higher risk of developing COVID-19 infection in Indian population. The comorbid conditions also predisposed individuals to COVID-19 and aggravated the infection.
Novel electrospun nanofibers incorporated with flavonoid glycosides from Glinus oppositifolius (L.) Aug. DC. for antibacterial dressings
Electrospun nanofibers are non-woven fibers with diameters in nano size, produced from natural or synthetic materials or a combination of both using electrospinning apparatus. These nanofibers find usage in different fields, especially in medical research and the healthcare sector for their varied properties. Apart from the core polymers which are essential for nanofiber formation, several other bioactive ingredients are complexed in nanofiber production to add value to the end product. In this current research work, flavonoid glycosides from the medicinal plant Glinus oppositifolius were extracted, purified using column and high-performance liquid chromatographic methods and tested for their antibacterial efficiency. The bioactive compounds were then blended in the nanofiber formulation to produce nanofiber-based anti-bacterial dressings through the electrospinning technique. The nanofibers were tested through FTIR for analyzing the composition of the final product and further subjected to scanning electron microscopical analysis to study morphology and nano size. The nanofibers thus produced exhibited strong anti-bacterial activity against tested pathogens namely K. pneumonia, P. vulgaris and Streptococcus sp. The positive results showed that further research can be carried out to optimize the production and medical dressing applications of the nanofiber.
An effective feature descriptor method to classify plant leaf diseases using eXtreme Gradient Boost
Identifying plant leaf diseases will be highly difficult due to the difficulties in gathering lesion characteristics from a quickly changing atmosphere, imbalanced illumination reflection of the incoming light source, and numerous other factors. A practical strategy for classifying plant leaf diseases is provided in this research. Using HSV, HU moments, and color histograms, we first created a leaf feature improvement framework that can enhance leaf characteristics in a complicated environment. Then, to increase feature classification capacity, a competent extreme boost method is modelled. Batch normalization is used to avoid network overfitting while also improving the model's resilience. The plant leaf disease feature improvement approach is favorable to boosting the efficiency of the XGBoost classification, as demonstrated in studies from various perspectives. For plant leaf disease photos obtained in the natural environment, our technique displays significant resilience, serving as a benchmark for the intelligent categorization of additional plant leaf diseases.
Design of efficient S-box for Advanced Encryption Standard
In digital era, data security is a necessary requirement. To establish secure communication modern encryption techniques plays a vital role. By employing an efficient S-box constraints of area, power and speed are achievable. In this paper method for efficient S-box is presented which provides promising solution in terms of required constraints. Comparison of proposed method with other existing method is also done by implementing it on field programmable gate array .It shows that proposed method uses only 6.14% slices resulting 13% improvement in comparison with other methods. Reduction in LUTs are done by 12.42 % in proposed method. Thus optimization is achieved in terms of number of slices and number of LUTs. Delay and memory usage is also reduced significantly.
Review of computational approaches to model transcranial direct current stimulations tDCS and its effectiveness
Neurological and psychological disorders are being treated by health professionals using medical technologies including drug therapy, electrical stimulation, and psychotherapy in some cases. Because of side effects caused by required drugs and social stigma for psychotherapy, these techniques have some limitations for their applicability in Mild cognitive impairment (MCI), Alzheimer’s disease (AD), Huntington disease (HD), dementia, major depressive disorder (MDD) and related neurological abnormalities. Â Transcranial direct current stimulation is a non-invasive brain stimulation (NIBS) technique that uses small currents to alter characteristics of a healthy and diseased neuron. Even though sophisticated tDCS devices are being used for treatment, treatment protocol and its efficacy is still a debatable question. Researchers have found ways to model tDCS computationally to know the outcome of treatment. This review provides details of computational approaches used to model tDCS. We have reviewed clinical and computational practices carried out by researchers to model treatment modality for tDCS.Â
The COVID-19 havoc and clues from Sex disaggregated data in the Indian population
The coronavirus infectious disease (COVID-19) has created a turmoil across the globe, with India emerging as one of the worst-hit countries. The Global scenario indicates a gender bias with a higher COVID-19 Case fatality rate (CFR) in males as opposed to females. However, countries like India, Nepal, Vietnam and Slovenia have reported a reverse trend in mortality. Real-time disaggregated data empowers countries to design more effective, sustainable, and people-centered approaches to treat and prevent COVID-19. Our study aimed to procure sex-disaggregated data in the Indian population by using a google form based online health survey. We have analyzed parameters like age, gender, occupation, sex and severity of infection based on CT score, steroid dependence, need for hospitalization, etc. The responses were evaluated by descriptive statistics by excluding arbitrary correlation. We found that the males were at a significantly greater risk of severe disease and mortality (~ twice) than females. We also found that the males as compared to females, presented almost eighteen times the odds of requiring intensive care unit (ICU) admission; reflecting severity of the infection. A sex-informed approach to COVID-19 research would reveal novel responses of the host immune system to SARS-CoV-2, which can then be utilized in formulation of policies for equitable health outcomes.
Synthesis, spectral studies and biological activity of novel 2-(substituted phenyl)-6-phenylimidazo[2,1-b]1,3,4-oxadiazole
Diverse series of 2-(substitutedphenyl)-6-phenylimidazo[2,1-b]1,3,4-oxadiazole were synthesized. Five of the synthesized compounds were evaluated for their anticancer activity on MCF-7 cancer cell lines. The recently synthesized compounds were illustrated by IR, 1HNMR. The anticancer activity of the compounds was carried out at Anti-Cancer Drug Screening Facility (ACDSF), Advanced Centre for Treatment, Research & Education in Cancer (ACTREC),Tata Memorial Centre, Kharghar, Navi Mumbai. The anticancer activity would be evaluated by In vitro testing using SRB assay protocols.All the screened compounds showed good to moderate activity against MCF-7 cancer cell line. Compound 5b, 6c, 7a were found to be active with GI50 <10 µg/ml.All the synthesized compounds were screened against Gram Positive and Gram Negative bacteria Streptococcus aureus, Bacillus subtilisand E.coli respectively.
Association of ABO blood group and antibody class with susceptibility and severity of COVID-19 infection in Indian Population
Since the COVID-19 eruption in December 2019, the investigation has been focused on its treatment and preventing the disease spread. Currently, there is no biomarker available that can predict the predisposition and severity of COVID-19 infection. In the present study, we have used the cross-sectional survey study data to decipher the association between the ABO blood group and susceptibility, severity and breakthrough COVID-19 infections. Further, we have also investigated the association between antibody class and the risk of contracting COVID-19 infection. Our results indicated that individuals with blood group B had higher susceptibility to acquire COVID-19 infection. In contrast, blood group A was found to be associated with a low risk of acquiring severe COVID-19. In addition, we did not find any correlation between ABO blood groups and breakthrough COVID-19 infections. Further, we examined the association of antibodies; anti-A (blood groups B and O) and anti-B (blood groups A and O) with COVID-19 infection. The analysis of antibody classes showed that anti-A antibody associated with a high predisposition to acquire COVID-19 infection. The present study indicates that blood group B and anti-A antibodies are associated with proneness to COVID -19 infection and severity.
Context aware human activity prediction in videos using Hand-Centric features and Dynamic programming based prediction algorithm
Activity prediction in videos deals with predicting human activity before it is fully observed. This work presents a context-aware activity prediction approach that can predict long-duration complex human activities from partially observed video. Here, we consider human poses and interacting objects as a context for activity prediction. The major challenges of context-aware activity predictions are to consider different interacting objects and to differentiate visually similar activity classes, such as cutting a tomato and cutting an apple. This article explores the use of hand-centric features for predicting human activity, consisting of various human-object interactions. A Dynamic Programming Based Activity Prediction Algorithm (DPAPA) is proposed for finding the future activity label based on observed actions. The proposed DPAPA algorithm do not employ Markovian dependencies or Hierarchical representation of activities, and hence is well suited for predicting human activities which are often Non-Markovian and Non-hierarchical. We evaluate results on MPPI Cooking activity dataset which consist of complex and long-duration activities.