Search

Phakomatoses are a group of congenital disorders characterised by hamartomatous lesions of the skin as well as the central and peripheral nervous systems. The presence of naevus flammeus or port-wine stain is a characteristic feature of many such disorders including Sturge-Weber syndrome (SWS), Klippel-Trenaunay syndrome (KTS) and Phakomatosis pigmentovascularis (PPV).We describe the ocular findings in a patient with coexisting PPV, SWS and KTS

This paper exemplifies the application of artificial neural network (ANN) for prediction of performances in adsorption of phenylic acid from waste water by conventional and low cost Lantana camara activated carbon as adsorbent material. To estimate the removal efficiencies of phenylic acid, a three-layer feed-forward neural network using a back propagation algorithm was utilised in the MATLAB environment. The initial concentrations (mg/L) of phenylic acid, amount (g/L) of adsorbent and pH are the input parameters utilised to train the neural network. The output of the neural network was taken to be the effectiveness of phenylic acid removal. Statistical measures like root mean square error and linear regression were also used to evaluate the effectiveness of the proposed ANN models. Based on the comparison of the removal efficiencies of contaminants using ANN models and empirical results, ANN modelling for the adsorption of phenolic compounds was found to be reasonably consistent with the empirical results.

Angiogenesis is crucial to the development of cancer because it allows the transport of oxygen, nutrients, and growth factors as well as the spread of tumors to distant organs. Inhibitors of angiogenesis prevent the formation of blood vessels that allow tumor cells to shrink, rather than promote tumor growth. Chitosan acts as a carrier for many drugs, since the compound has various properties such as biodegradable, less toxicity, more stable, simple, easy to prepare, and biocompatible. The aim of the current study was to evaluate the efficacy of chitosan nanoparticles encapsulated with troxerutin (Chi-Trox NPs) against angiogenesis and cancer in ova chick chorioallantoic membrane (CAM) model. Chi-Trox NPs were synthesized using a nanoprecipitation method and were characterized by various analyses. 24 hours’ fertilized eggs (6 eggs/group) were treated with native Trox and Chi-Trox NPs for 5 days. The antiangiogenic activity was evaluated by morphometric, histopathological, immunohistochemical (CD104 and vimentin), and mRNA expression of MMP and FGF2 using RT-PCR. The anticancer activity was evaluated by histopathological, immunohistochmical (CD44), and mRNA expression of FGF2 and MMP. The synthesized chitosan NPs were successfully encapsulated with troxerutin, and the loading efficiency of chitosan NPs was found to be 86.4 ± 0.12 % and 13.2 ± 0.16 % respectively. Morphometric analysis of Chi-Trox NPs showed a considerable decrease in the number of blood vessels compared with control and native Trox. The histopathological observation of CAM confirmed that Chi-Trox NPs induce a significant reduction in inflammatory cells and the thickness of blood capillaries compared to control and native Trox. The immunohistochemical evaluation of CAM revealed Chi-Trox decreased CD104, vimentin and CD44 protein levels were compared with control and native Trox. Furthermore, the mRNA expression levels of FGF2 and MMP were significantly downregulated compared to their native forms. From the obtained results, Chi-Trox NPs possess significant inhibition of angiogenesis and can be used as therapeutic agents for cancer in the future.

Glaucoma is a condition of the eye that is caused by an increase in the eye’s intraocular pressure that, when it reaches its advanced stage, causes the patient to lose all of their vision. Thus, glaucoma screening-based treatment administered in a timely manner has the potential to prevent the patient from losing all of their vision. However, because glaucoma screening is a complicated process and there is a shortage of human resources, we frequently experience delays, which can lead to an increase in the proportion of people who have lost their eyesight worldwide. In order to overcome the limitations of current manual approaches, there is a critical need to create a reliable automated framework for early detection of Optic Disc (OD) and Optic Cup (OC) lesions. In addition, the classification process is made more difficult by the high degree of overlap between the lesion and eye colour. In this paper, we proposed an automatic detection of Glaucoma disease. In this proposed model is consisting of two major stages. First approach is segmentation and other method is classification. The initial phase uses a Stacked Attention based U-Net architecture to identify the optic disc in a retinal fundus image and then extract it. MobileNet-V2 is used for classification of and glaucoma and non-glaucoma images. Experiment results show that the proposed method outperforms other methods with an accuracy, sensitivity and specificity of 98.9%, 95.2% and 97.5% respectively.

The range of diagnostic equipment has been widened and improved by the quick development of biomedical research technologies. The creation of multifunctional instruments that become essential for biomedical operations has been discovered by several research organizations to be made possible by optical imaging, acoustic image analysis, and magnetic resonance imaging. One of the most crucial tools is hyperspectral photoacoustic (PA) imaging, which combines optical and ultrasonic technology. In this study, the reconstruction of the PA pictures employs a new deployment of deep learning methods. This enabled us to train and evaluate our deep-learning approach under several imaging situations in addition to firmly establishing the contextual information. This study presents an optimization approach that blends multispectral optical acoustic imaging with detailed transfer learning-based diagnostic imaging. The particle swarm-convolutional neural network (PS-CNN) technique aims to reconstruct and categorize the presence of cancer using ultrasonic pictures. In image processing, the technique of bilateral filtration (BF) is commonly employed to remove noise. Additionally, the biological images are separated using portable LED Net frameworks. It is also possible to employ a feature extraction technique with the PS optimization methodology. Last but not least, biological images employ a CNN model to assign suitable classification. Using a standard dataset, the PS-CNN technology’s efficacy is confirmed, and testing findings revealed that it performs superior to other methods.

Introduction and Aim: The patients who have been suffered with stroke have severe balance problems, it is difficult to tell with certainty how balance performance in stroke patients is impacted by vestibular impairment. The study's purpose was to determine if oculomotor and gaze stability exercises may help post-stroke individuals improve their balance. Materials and Methods: This study includes all articles of various databases from 2000 to 2020 was included for study selection. The google scholar, research gate databases were comprehensively studied. 100 articles were searched and out of which 60 articles were selected to be assessed for eligibility in full text. Finally, 9 articles were chosen for inclusion and for analysis, although 7 of them did not match our proposed inclusion criteria. Results: Exercises for the vestibular system have been demonstrated to be effective for stroke patients whose balance has been compromised. Conclusion: The study finds that more high -quality experimental research on vestibular exercises and balance impairment after stroke is necessary because there is very little evidence from earlier experimental studies to draw firm conclusions about their effects.

Words can convey subtle cultural stereotypes and perpetuate subconscious gender biases. Wording in job advertisements that appeals to 1 gender and deters others may unintentionally skew the applicant pool and affect the early phase of the recruitment process. "Masculine" tone can lead to decreased interest among women applicants while 'feminine' wording may not affect a man's decision to apply for the job. In this study, we evaluated the presence and extent of subtle gender bias in job advertisements for radiology faculty positions.All job postings for faculty radiologists were retrieved from the American College of Radiology Career Center website in July 2020. The complete job advertisement was analyzed using Gender Decoder, a publicly available web-based application, to determine number and percentage of female or male coded words and the overall tone of the ad. The job posts were also stratified by subspecialty, leadership positions and academic versus private practice environments.Of the 623 job postings reviewed, a little over half (52.0%) of job postings were feminine coded, 26.6% had a masculine tone and 21.3% had a neutral tone. Of the leadership (division director) positions, 50.0% (4/8) had a masculine tone, 37.5% had a feminine tone, and 12.5% had a neutral tone. Among various specialties, pediatric radiology had the lowest percentage of job posts with a masculine tone (10.5%) while nuclear medicine had the highest percentage (41.7%). The most commonly used feminine words were: "support," "responsible," "commitment" and the most common masculine words were: "competitive," "leader," "active."Most of the imaging job advertisements were feminine coded, with masculine tone in overall 26.6% posts and neutral tone in about a fifth. Leadership posts had a higher percentage of masculine tone. Awareness of these biases is important to enable diversity in recruitment and to ensure a diverse applicant pool.

Financial fraud is a significant problem in the banking industry, and detecting fraudulent transactions is a critical task for banks to protect their customers and maintain trust in the financial system. Traditional rule-based approaches for detecting fraud often rely on pre-defined thresholds and heuristics, which can be circumvented by sophisticated fraudsters. As a result, machine learning techniques have gained increasing attention in recent years for their ability to automatically learn from data and adapt to changing fraud patterns .In this project, we propose a novel approach for classifying fraudulent banking transactions using a deep learning algorithm. Our approach leverages the power of deep neural networks to automatically extract meaningful features from transaction data, and then use these features to accurately classify transactions as either fraudulent or legitimate

Background: Reducing the burden of death due to infection is an urgent global public health priority. Previous studies have estimated the number of deaths associated with drug-resistant infections and sepsis and found that infections remain a leading cause of death globally. Understanding the global burden of common bacterial pathogens (both susceptible and resistant to antimicrobials) is essential to identify the greatest threats to public health. To our knowledge, this is the first study to present global comprehensive estimates of deaths associated with 33 bacterial pathogens across 11 major infectious syndromes. Methods: We estimated deaths associated with 33 bacterial genera or species across 11 infectious syndromes in 2019 using methods from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, in addition to a subset of the input data described in the Global Burden of Antimicrobial Resistance 2019 study. This study included 343 million individual records or isolates covering 11 361 study-location-years. We used three modelling steps to estimate the number of deaths associated with each pathogen: deaths in which infection had a role, the fraction of deaths due to infection that are attributable to a given infectious syndrome, and the fraction of deaths due to an infectious syndrome that are attributable to a given pathogen. Estimates were produced for all ages and for males and females across 204 countries and territories in 2019. 95% uncertainty intervals (UIs) were calculated for final estimates of deaths and infections associated with the 33 bacterial pathogens following standard GBD methods by taking the 2·5th and 97·5th percentiles across 1000 posterior draws for each quantity of interest. Findings: From an estimated 13·7 million (95% UI 10·9–17·1) infection-related deaths in 2019, there were 7·7 million deaths (5·7–10·2) associated with the 33 bacterial pathogens (both resistant and susceptible to antimicrobials) across the 11 infectious syndromes estimated in this study. We estimated deaths associated with the 33 bacterial pathogens to comprise 13·6% (10·2–18·1) of all global deaths and 56·2% (52·1–60·1) of all sepsis-related deaths in 2019. Five leading pathogens—Staphylococcus aureus, Escherichia coli, Streptococcus pneumoniae, Klebsiella pneumoniae, and Pseudomonas aeruginosa—were responsible for 54·9% (52·9–56·9) of deaths among the investigated bacteria. The deadliest infectious syndromes and pathogens varied by location and age. The age-standardised mortality rate associated with these bacterial pathogens was highest in the sub-Saharan Africa super-region, with 230 deaths (185–285) per 100 000 population, and lowest in the high-income super-region, with 52·2 deaths (37·4–71·5) per 100 000 population. S aureus was the leading bacterial cause of death in 135 countries and was also associated with the most deaths in individuals older than 15 years, globally. Among children younger than 5 years, S pneumoniae was the pathogen associated with the most deaths. In 2019, more than 6 million deaths occurred as a result of three bacterial infectious syndromes, with lower respiratory infections and bloodstream infections each causing more than 2 million deaths and peritoneal and intra-abdominal infections causing more than 1 million deaths. Interpretation: The 33 bacterial pathogens that we investigated in this study are a substantial source of health loss globally, with considerable variation in their distribution across infectious syndromes and locations. Compared with GBD Level 3 underlying causes of death, deaths associated with these bacteria would rank as the second leading cause of death globally in 2019; hence, they should be considered an urgent priority for intervention within the global health community. Strategies to address the burden of bacterial infections include infection prevention, optimised use of antibiotics, improved capacity for microbiological analysis, vaccine development, and improved and more pervasive use of available vaccines. These estimates can be used to help set priorities for vaccine need, demand, and development. Funding: Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care, using UK aid funding managed by the Fleming Fund.

The main objective of today’s manufacturing industries is to produce low cost and high quality product in short time. Turning is a very important machining process in which a single-point cutting tool removes material from the surface of a rotating cylindrical work-piece. In general, CNC lathe machine is operated with several controllable factors such as spindle speed, depth of cut, feed rate etc. This present work focuses on CNC turning of aluminum alloy 6063 using carbide tool for varying feed rate. The results revealed that the feed rate is the most important parameter that affects the material removal rate, surface finish and machine time in CNC turning process.

Background: Aureobasidium pullulans (black yeast) AFO-202 strain-produced beta glucan, Nichi Glucan, has been shown to improve the behavior and sleep pattern along with an increase in α-synuclein and melatonin in children with autism spectrum disorder (ASD). Objective: In this randomized pilot clinical study, we have evaluated the gut microbiota of subjects with ASD after consumption of Nichi Glucan. Methods: Eighteen subjects with ASD were randomly allocated: six subjects in the control group (Group 1): conventional treatment comprising remedial behavioral therapies and L-carnosine 500 mg per day, and 12 subjects (Group 2) underwent supplementation with Nichi Glucan 0.5 g twice daily along with the conventional treatment for 90 days. Results: Whole genome metagenome (WGM) sequencing of the stool samples at baseline and after intervention showed that among genera of relevance, the abundance of Enterobacteriaceae was decreased almost to zero in Group 2 after intervention, whereas it increased from 0.36% to 0.85% in Group 1. The abundance of Bacteroides increased in Group 1, whereas it decreased in Group 2. The abundance of Prevotella increased while the abundance of Lactobacillus decreased in both Group 1 and Group 2. Among species, a decrease was seen in Escherichia coli, Akkermansia muciniphila CAG:154, Blautia spp., Coprobacillus sp., and Clostridium bolteae CAG:59, with an increase of Faecalibacterium prausnitzii and Prevotella copri, which are both beneficial. Conclusion: AFO-202 beta 1,3–1,6 glucan, in addition to balancing the gut microbiome in children with ASD and its role in effective control of curli-producing Enterobacteriaceae that leads to α-synuclein misfolding and accumulation, may have a prophylactic role in Parkinson’s and Alzheimer’s diseases as well.

Amritotharanam Kashyam, a specific Ayurvedic drug, was the focus of the current inquiry to evaluate its efficacy. For liver and digestive-related issues, this medication is suggested. This was obtained from a standard Ayurvedic vendor in Chennai (India), and GC-MS analysis was carried out according to the standard procedure. A few critical biomolecules include benzoic acid, hexadecanoic acid, 6,9-octadecadienoic acid, 9-octadecenoic acid, methyl ester (E)-, heptadecanoic acid, 16-methyl, methyl ester, methyl 18-methylnonadecanoate, tetracosanoic acid, distearin, hexadecanoic acid, and 1-(hydroxymethyl)-1,2-ethanediol ester. The obtained biomolecules exhibited some significant therapeutic functions, including acidification, inhibition of arachidonic acid formation, increase in the aromatic amino acid decarboxylase, suppression of uric acid generation, inhibitors of catechol-O-methyltransferase, urine acidifiers, etc. The anticancer and antiviral potential of these phytocompounds were investigated using molecular docking and dynamics. The phytocompounds pharmacokinetic characteristics were investigated using ADME analysis. Through docking and dynamics simulation, in silico tests demonstrated the phytocompounds' inhibitory efficiency against the target proteins. These functions reasonably relate to the medicinal function of Amritotharanam Kashyam. The MTT assay findings demonstrated this medication’s anticancer effects. The ability to be an effective drug is demonstrated by its antioxidant, anti-inflammatory, and membrane-stabilizing properties.

The intrusion is considered a significant problematic parameter in Cloud networks. Thus, an efficient mechanism is required to avoid intrusion and provide more security to the cloud system. Therefore, the novel Artificial Bee-based Elman Neural Security Framework (ABENSF) is developed in this article. The developed model rescales the raw dataset using the pre-processing function. Moreover, the artificial bee's optimal fitness function is integrated into the feature extraction phase to track and extract the attack features. In addition, the monitoring mechanism in the developed model provides high security to the network by preventing attacks. Thus, the tracking and monitoring functions avoid intrusion by eliminating known and unknown attacks. The presented work was designed and validated with an NSL-KDD dataset in python software. Finally, the performance parameters of the presented work are estimated and verified with the existing techniques in a comparative analysis. The comparative performance shows that the developed model has earned better outcomes than others.

The technological development of Internet of Things (IoT) applications is emerging and attracting the attention of the real world in the automated industry, agriculture, environment, and scientific community. In most scenarios, extending the network lifetime of an IoT network is highly challenging because of constrained nodes. The wireless sensor network (WSN) is the core component of IoT applications. In addition, the WSN nodes are required for the network processes, particularly routing, energy maintenance, load balance, congestion control, packet delivery, quick response, and more. The failure of any of the above network processes will affect the entire network operation. IPv6 Routing Protocol for Low-power and Lossy network (RPL) provides high routing solutions to IoT applications requirements. The load balance, congestion control, traffic load, and bottleneck problems are still open issues in the RPL. To resolve the load balance issue, we propose a weighted sum method objective function (WSM-OF), which provides the ability to select the alternative parent in routing by RPL metrics. WSM-OF adopts congestion control and load balancing to avoid heavy traffic and extend the network's node lifetime. The network parameters of control overhead, jitter, packet delivery ratio, parent switching, energy consumption, latency, and network lifetime are implemented and analyzed through the COOJA simulator. The result shows that the WSM-OF improves the network performance and significantly enhances the network lifetime by up to 7.8%.

The mechanical properties of natural fiber composites reinforced with rice husk and multi-walled carbon nanotubes are studied. Two different mixing concentrations are prepared: 5 wt.% and 10 wt. % rice husk and 0.5 wt.% and 1.0 wt.% multi-walled carbon nanotubes incorporating with the constant reinforcing phases of the banana fiber is 10 wt.%. (Specimens: NBRME1 and NBRME2, where, N-NaOH; B-Banana fiber; R-Rice husk; M-MWCNT; E-Epoxy). The effect of surface treatment of banana fiber bundles is reacted with a 6% sodium hydroxide solution. The surface-treated reinforcement results indicated higher tensile, bending, and impact strength of the 0.5% MWCNT composite (43.96 MPa, 60.62 MPa, and 46.5 J/m) compared with 1% MWCNT composite. High-resolution optical macroscopic images are revealed a variety of defects, including interface behaviour, fiber stretching, fracture, cracking, and agglomeration. Biodegradable organic rice husk-based epoxy resin composite is valuable for manufacturing electronic parts, chips and circuits.

Natural fiber composites are prepared by combining two varying fiber/filler contents with a single matrix material. Hybrid polymer composite material made with synthetic agents (Thermosetting polymer) and natural fiber/filler contents. The current study focuses on hybrid polymer composite Multi Walled Carbon nanotubes (MWCNT)-filled Banana fiber/Rice husk filler-reinforced composites that have been assessed for mechanical responses. The banana fiber mat has been in the constant piling mode and altering the MWCNT and rice husk percentage in volume. Vacuum lay-up technique was used to construct the hybrid polymer composite and MWCNT was included with the help of ultrasonic probe sonicator instrument. Mechanical testing is performed accordance to the ASTM standards. The integration of MWCNT, as well as the constant piling order of banana fiber layers, has a stronger impact on mechanical properties. The three samples are prepared at the extremities 5% Banana fiber mat uniform loading and rice husk proportions are 10%, and 15% along with MWCNT filler (0.5% and 1%) for BRME2 and BRME3. The exhibited mechanical testing of the hybrid polymer composite to identify the conventional enhancement of hardness, tensile, impact, flexural, and compressive properties. Scanning Electron Microscope (SEM) is used to examine the fractograph of the tensile sample prepared with BRME3. BRME2 polymer composite with 1% weight of MWCNT was identified higher mechanical properties compare to BRME3 and BRME1 (Tensile, Flexural Strength, and Shore-D Hardness: 46.5 MPa, 59.7 MPa, and 88.5). Furthermore, the impact strength of BRME3 was significantly higher than BRME2 and BRME1.

Hybrid Energy System (HES) is becoming popular as it is identified as the most safe, sustainable, and long-term option for energy management. The grid-connected power network is vulnerable to frequent interruptions, which can lead to instability and a huge blackout. Furthermore, the presence of switching devices in industrial and domestic applications causes disturbances such as voltage swell/sag, waveform distortion, interruptions, impulsive voltage, and so on. Dynamic voltage restorer, known as a modern power compensating device, is cost effective, compact in size and can handle more energy capacity than DTSATCOM and UPFC. Hence for addressing the power quality issues in hybrid solar photovoltaic (PV)-proton exchange membrane fuel cells (PEMFC) system, dynamic voltage restorer (DVR) has been designed in this paper. As the fuzzy logic technique can provide flexible solutions in the case of uncertainty, fuzzy logic-based controller has also been employed with DVR to regulate the firing pulses of a PWM generator. Based on the simulation results obtained using fast fourier transform (FFT) analysis in MATLAB/Simulink, the dynamic performance of HES against the most important contingency condition or three phase faults is analyzed in detail. Further, the impact of DVR on power quality improvement under severe fault has also been demonstrated in this research work. On analyzing the total harmonic distortion (THD) values of HES under heavy disturbances, the fuzzy controlled dynamic voltage regulator (FCDVR) is identified as an efficient control mechanism for enhancing power quality in grid-connected HES.

Actinomycetes are one of the important secondary metabolite producers. Researchers focused on the exclusive marine areas for isolation and identification of marine actinomycetes. The present study focused on the isolation and identification of Nocardiopsis dassonovillei (ON627850) from TS Pettai region. The potential strainTSP1 showed effective antibacterial activity against Haemophilus influenza. TSP1 isolates showed IC50 value of 75.22 μg/ml effective antioxidant activity determined by DPPH assay. Cytotoxicity assay results were noted for the ethyl acetate extract of TSP1 screened against oral cancer cell lines (KB). The spectral characterization studies of UV, FT-IR and GC-MS results identified the compound 2,4-di-tert-butylphenol. The multi-potential 2,4-di-tert-butylphenol compound finally docked with KB cell lines protein for drug discoveries.

Among the diverse areas of 3D printing, high-quality silicone printing is one of the least available and most restrictive. However, silicone-based components are integral to numerous advanced technologies and everyday consumer products. We developed a silicone 3D printing technique that produces precise, accurate, strong, and functional structures made from several commercially available silicone formulations. To achieve this level of performance, we developed a support material made from a silicone oil emulsion. This material exhibits negligible interfacial tension against silicone-based inks, eliminating the disruptive forces that often drive printed silicone features to deform and break apart. The versatility of this approach enables the use of established silicone formulations in fabricating complex structures and features as small as 8 micrometers in diameter.

A new drug takes a long time and is expensive to introduce.By using insilico drug design, you can save time and money. Utilizing computational software, a novel Schiff's base Dibenzosuberenone derivative was designed and molecular docking studies were performed using autodock software. To predict Absorption, Distribution, Metabolism, Excretion, and Molecular Properties of Dibenzosuberenone derivatives, insilico screening was performed. It should be examined how its Dibenzosuberenone derivatives interact with specific targets. The Dibenzosuberenone derivatives were successfully identified as targets in this study.

Electro discharge machining (EDM) is a cycle for molding tough materials and framing profound contour formed openings by warm disintegration in all sort of electrically conductive materials. The goal of the venture to be concentrating because of working parameters of EDM for machining of silicon nitride-titanium nitride in the machining qualities with copper electrode, for example input Spark on time (Son), current (Ip), Spark off time (Soff), spark gap and dielectric pressure on the metal removal rate (MRR) and Electrode Wear Rate (EWR) were analyzed. Subsequently, using Taguchi analysis of various plots like Mean effect plots, Interaction plots, and contour plots, performance characteristics are looked at in relation to multiple process factors. Fuzzy logic and Regression analysis is utilized to combine various reactions into a solitary trademark record known as the Multi Response Performance Index (MRPI).The trial and anticipated qualities were in a decent programming instrument for discovering the MRPI esteem. For numerous performance aspects, such as material removal rate, electrode wear rate and so on, the optimal process parameter combination was established using fuzzy logic analysis. The key process factors, which included spark off time and current, were found using an ANOVA based on a fuzzy algorithm. Topography on machined surface and cross-sectional view of conductive Si3N4-TiN composite and surface characteristics of machined electrode is examined by SEM analysis and identified the best hole surface and worst hole surface. Sensitivity analysis is being utilized to determine how much the input values, such as Ip, Son and Soff, will need to alter in order to get the desired, optimal result. In the complexity analysis, each constraint of the machine, composite and process is addressed. Future researches might look into various electrodes to assess geometrical tolerances including angularity, parallelism, total run out, flatness, straightness, concentricity, and line profile employing other optimization methodologies to achieve the best outcome. The findings of the confirmatory experiment have been established, indicating that it may be feasible to successfully strengthen the spark eroding technique.

Farmers’ risk preferences play an important role in agricultural production decisions, risk takers means the farmers who are willing to take risky decisions in farming, risk aversion means an attitude of reluctance to take risky decisions in farming. Climatic change effects all regions across the globe and causes substantial agitations that can be expected to be natural systems that have foreseeable influences on the economic systems of upland regions through both direct and indirect means. Risk preferences reflect the farmers’ personal experiences and beliefs, these preferences explain how the decision-maker assesses and react to risks. This study characterizes risk behaviour among marginal and small farmers in Cauvery Delta Zone and determines how these risk preferences affects the farmers. The study was conducted in Cauvery Delta zone of Thanjavur, Thiruvarur and Nagapattinam districts with a Sample size of 366 farmers which consists of 183 marginal and 183 small farmers was selected randomly based on proportionate random sampling method. The risk behavior was measured by the measure of risk attitude and two lottery methods viz., Eckel-Grossman and Holt-Laury based lottery method. Measure of risk attitude results shows that, 27.60 per cent of farmers were moderate risk taker followed by 24.30 per cent were risk averser and 15.00 per cent of farmers were risk taker. The Eckel and Grossman lottery method result shows CRRA (Constant Relative Risk Aversion) value was 0.38 to 0.67, which shows that marginal farmers were risk aversers and small farmers were moderate risk takers. The CRRA adapted from Holt and Laury [1] range for the maximum was 1.37 and minimum -1.71 for their choices. The majority of marginal farmers were risk aversers, the socio-economic characteristics of the farmers decides the risk preference. The risk-averse farmer this may imply risk-taking behavior that is reduced by risk aversion (resulting in on-farm risk management strategies) and a reduced demand of insurance.

The Severe Acute Respiratory Syndrome (SARS) are caused by the strain of the corona virus causes cold and influenza. In recent years, the covid pandemic spread throughout the world killing millions of people. The fatality rate has increased and it also leads to pneumonia for breathing problems. Several methods like wavelet filter banks, time series methods, Neural networks was developed for the diagnosis of severe acute respiratory syndrome coronavirus, still the accuracy can be improved. Less works is carried out for hardware implementation for syndrome detectors. This proposed work represents the FPGA (Field Programmable Gate Array) implementation of the hybrid method using Convolutional Recurrent neural network and Independent Components Analysis (ICA). The architecture extracts the ccomplex features from ECG (Electrocardiogram) samples. The hybrid Statistical and Recurrent Neural Network (RNN) Architecture implementation in a real time hardware detects the Severe Acute Respiratory Syndrome presented. The proposed method can be implemented in MATLAB, Embedded and DSP (Digital Signal Processor). But, the FPGAs consume less power computationally efficient. Since, ICA is an efficient method due to its blind source separation property accumulate the extraction of features accurate described. The mathematical model for the analysis of ECG signal using RNN is analyzed and based on that the proposed model is selected. On investigation the hybrid method using the statistical and neural network model is efficient in the analysis of biomedical signal especially ECG. The proposed ICA based RNN model is mathematically evaluated and tested with real time data. For implementation, Quartus software is used for effectiveness of the proposed model.

There are a number of techniques available to reduce the engine vibration, and vibration isolation is one among those techniques. Such vibrations can be isolated using hybrid engine mounts that absorb the forces caused by vibration. Consequently, the present work proposes a hybrid aluminium mount filled with silica gel to isolate the engine vibration. Experiments were carried out on the variable compression ratio engine mounted on the hybrid honeycomb structure, and the free, forced vibrations, and frequency domains were analysed. The test results portrayed that the performance of the engine with a hybrid mount is found to be better than the conventional rubber mount. The hybrid sandwich panel with a honeycomb structure crowded with silica gel as fibrous material was utilized to isolate the engine vibrations. Compared with conventional mount (1.502 m/s2), high amount of vibration was reduced by honeycomb structured mount (0.814 m/s2) using different load conditions and fuel input pressure of 150 bar. Vibration in conventional mount for blower closing condition was 1.546 m/s2 and that in honeycomb structured mount was 1.4 m/s2.

Despite data showing that estrogen is vasculoprotective in large conduit arteries, hormone therapy (HT) during menopause has not proven to mitigate cardiovascular disease (CVD) risk. Estrogen exposure through prolonged oral contraceptive use and gender-affirming therapy can also increase cis- and trans- females' risk for future CVD, respectively. The microvasculature is a unique vascular bed that when dysfunctional can independently predict future adverse cardiac events, however, studies on the influence of estrogen on human microvessels are limited. Here we show that isolated human arterioles from females across the lifespan maintain nitric oxide (NO) mediated dilation to flow whereas chronic (16-20hrs) exposure to exogenous (100 nM) 17β-estradiol promotes microvascular endothelial dysfunction in vessels from adult females40 yrs and ≥40 yrs of age. The damaging effect of estrogen was more dramatic in arterioles from biological males, as they exhibited both endothelial and smooth muscle dysfunction. Further, females40yrs have greater endothelial expression of estrogen receptor beta (ER-β) and G-protein coupled estrogen receptor (GPER) compared to females ≥40 yrs and males. Estrogen receptor alpha (ER-α), the prominent receptor associated with protective effects of estrogen, was identified within the adventitia as opposed to the endothelium across all groups. To our knowledge, this is the first study to report detrimental effects of estrogen on the human microvasculature and highlights differences in estrogen receptor expression.

Background: Variability in the pharmacokinetics and pharmacodynamics of oxycodone in children undergoing surgery could be due to genetic polymorphisms. Materials & methods: The authors studied the association between clinical outcomes and pharmacogenes in children undergoing major surgery. A total of 89 children (35 undergoing pectus excavatum repair and 54 undergoing spinal fusion) were recruited. Results: OPRM1 SNP rs6902403 showed an association with maximum pain score and total morphine equivalent dose (p Clinical trial registration: NCT03495388 ( ClinicalTrials.gov )

In recent years, the emphasis has shifted toward more precious minerals and composite structures to produce thinner, cheaper, more suitable materials for specific applications. The primary purpose of this study would be to determine how any walnut fillers perform in a vulgare biocomposite. The nanocomposite was created employing the most economical manual lay-up method. To accomplish the legitimate goal, the following descriptions have been levied: At different levels, (i) volume ratios of luffa thread, (ii) weight ratios of walnut powder, as well as (iii) freezing processing durations, all are accessible. The multilayer aggregates are in a freezing room set to 77 degrees Celsius. Tension and elastic modulus were tested mechanically. The morphological properties of powdered particles were detected using a scanning electron microscope. The mechanical integrity of 10% walnut shell powder, 40% luffa fabric, as well as 15-minute freezing treatments, surpasses 5 and 15% walnut shell granules.