Bacteria-mediated green synthesis of silver nanoparticles and their antifungal potentials against Aspergillus flavus.

The best biocontroller Bacillus subtilis produced silver nanoparticles (AgNPs) with a spherical form and a 62 nm size through green synthesis. Using UV-vis spectroscopy, PSA, and zeta potential analysis, scanning electron microscopy, and Fourier transform infrared spectroscopy, the properties of synthesized silver nanoparticles were determined. Silver nanoparticles were tested for their antifungicidal efficacy against the most virulent isolate of the Aspergillus flavus fungus, JAM-JKB-BHA-GG20, and among the 10 different treatments, the treatment T6 [PDA + 1 ml of NP (19: 1)] + Pathogen was shown to be extremely significant (82.53%). TG-51 and GG-22 were found to be the most sensitive groundnut varieties after 5 and 10 days of LC-MS QTOF infection when 25 different groundnut varieties were screened using the most toxic Aspergillus flavus isolate JAM- JKB-BHA-GG20, respectively. In this research, the most susceptible groundnut cultivar, designated GG-22, was tested. Because less aflatoxin (1651.15 g.kg-1) was observed, treatment T8 (Seed + Pathogen + 2 ml silver nanoparticles) was determined to be much more effective. The treated samples were examined by Inductively Coupled Plasma Mass Spectrometry for the detection of metal ions and the fungicide carbendazim. Ag particles (0.8 g/g-1) and the fungicide carbendazim (0.025 g/g-1) were found during Inductively Coupled Plasma Mass Spectrometry analysis below detectable levels. To protect plants against the invasion of fungal pathogens, environmentally friendly green silver nanoparticle antagonists with antifungal properties were able to prevent the synthesis of mycotoxin by up to 82.53%.

Prevalence of human papillomavirus infection and associated factors among women attending cervical cancer screening in setting of Addis Ababa, Ethiopia.

Human papillomaviruses (HPVs) are circular, nonenveloped small double-stranded DNA viruses that infect stratified epithelium and can cause a number of life-threatening diseases. HPV is the central risk factor for developing cervical cancer and is estimated that approximately 98% of this disease is associated with oncogenic types of HPV. HPV infection leads to an estimated 266,000 cervical cancer deaths annually. Therefore, the objective of this study was to determine the prevalence of HPV infection and risk factors associated with cervical lesion among women attending the cervical cancer screening clinic at the Ethiopian Family Guidance Association, Addis Ababa. A cross-sectional study was conducted to determine the prevalence of HPV infection. Data were collected using a questionnaire and samples leftover from cervical screening were taken. The leftover swab was air dried and DNA was extracted and amplified by using a PCR. A total of 247 women were included in the study. The prevalence of HPV was 9.72% among the population studied. Of all participants, 27.13% were positive for cervical intraepithelial neoplasia-1 (CIN1). CIN1 positivity was found in half of HPV positive women. Among HPV positive women, half of them had started sexual intercourse at ages 12-17 years and 41.66% were women who gave birth at ages 12-17 years. The high prevalence of HPV and the CIN1 positive group were ages 36-57 and women with multiple sexual partners. The other groups with the highest CIN1 positive were 22.39% grade (9-12) and 20.9% primary (1-8) and uneducated women. Among HPV positive women, 83.33% had an abortion history and 80% miscarried in the first trimester. Among the CIN1 positives, 53.73% had more than two sexual partners. Among HPV positive women, half of them were users of contraception methods. In conclusion, the highest prevalence of HPV is among women who began sexual intercourse earlier and who gave birth at 12-17 years of age, have an abortion history, with MSP and oral contraceptive methods users. In addition to HPV, early pregnancy and sexual intercourse at 12-17 years of age, abortion, MSP, and oral hormonal contraceptives are factors in cervical cancer. Finally, most women do not have enough knowledge and awareness about cervical cancer and the risk factor.

Whole genome sequencing and annotation of Aspergillus flavus JAM-JKB-B HA-GG20.

Groundnuts are mostly contaminated with the mold Aspergillus flavus which produces a carcinogenic mycotoxin called as aflatoxin. It is very important to understand the genetic factors underlying its pathogenicity, regulation, and biosynthesis of secondary metabolites and animal toxicities, but it still lacks useful information due to certain gaps in the era of modern technology. Therefore, the present study was considered to determine the key genes and metabolites involved in the biosynthesis of aflatoxin by using a molecular approach in a virulent strain of Aspergillus. The whole genome sequence of highly toxic and virulent Aspergillus isolates JAM-JKB-B HA-GG20 revealed 3,73,54,834 bp genome size, 2, 26, 257 number of contigs with N50 value of 49,272 bp, 12,400 genes and 48.1% of GC contained respectively. The genome sequence was compared with other known aflatoxin producing and non-producing genome of Aspergillus spp. and 61 secondary metabolite (SM) gene clusters were annotated with the toxic strain JAM-JKB-BHA-GG20 which showed similarity with other Aspergillus spp. A total number of eight genes (ver-1, AflR, pksA, uvm8, omt1, nor-1, Vha and aflP) were identified related to biosynthesis of aflatoxin and ochratoxin. Also, 69 SSR with forward and reverse primers and 137 di and tri nucleotide motifs were identified in the nucleotide sequence region related to aflatoxin gene pathway. The genes and putative metabolites identified in this study are potentially involved in host invasion and pathogenicity. As such, the genomic information obtained in this study is helpful in understanding aflatoxin gene producing pathway in comparison to other Aspergillus spp. and predicted presence of other secondary metabolites clusters viz. Nrps, T1pks etc. genes associated with a biosynthesis of OTA mycotoxin.

Phytochemical profiling, antibacterial and antioxidant potential of different parts of invasive plant Sonchus wightianus DC.

The aim of the present investigation was to elucidate the phytochemical characterisation, antimicrobial and antioxidant efficacies of methanol extracts of leaf, stem, and root parts of S. wightianus. The phytochemical characterisation by GC-MS analysis revealed the presence of different phytocompounds such as Lupenone (38.02% in leave extract), Leupeyl acetate (33.43% in root extract), 24- Noursa -3, 12- diene (27.16% stem extract). S. wightianus methanol extracts demonstrate IC50, 8.52 µg/ml, 7.72 µg/ml and 9.08 µg/ml using DPPH, FRAP and MCA scavenging assay, respectively. The antibacterial activity of different methanol extracts was evaluated against four microorganisms with a mean zone diameter of inhibition ranging from 13 to 19 mm. For quantitative phytochemical analysis, the highest Total Phenolic Content (TPC) and Total Flavonol Content (TFC-2) were found for steam methanol extract (321.7 mg GAE/g of dry extract, 162.4 mg QE/g of dry extract respectively).

De novo transcriptome sequencing of drought tolerance-associated genes in little millet (Panicum sumatrense L.).

The genome size of the little millet Panicum sumatrense is unknown, although its genome is fairly diploid (2n = 4x = 36). Despite tremendous nutritional value and adaptability to adverse climatic conditions, P. sumatrense use was limited by their low palatability, coarse grain, and lack of variety of culinary preparations. Hence, understanding how to vary their usage to offer food and nutritional security in the continuously changing modern world, the proposed study was aimed to determine potential genes and metabolites implicated in drought resistance. The drought-resistant genotype of tiny millet OLM-203/Tarini was offered in pots under both relaxed and demanding circumstances. The experimental seedlings were 32 days old and had been under water stress for 23 days. A total of 7606 genes were compared between 23 and 32 days for roots and 7264 total genes were compared between 23 and 32 days for leaves, according to a research on differential expression genes (DEGs). Twenty essential genes for drought tolerance were up-or down-regulated in the control and treated roots of the OLM-203 genotype. For instance, the genes RS193 and XB34 were up-regulated in leaves while, WLIM1 was found to be down-regulated. Gene SKI35 was up-regulated in roots, whereas MPK6 and TCMOp1 were down-regulated in root samples. The roots and leaves of the tiny millet OLM-203 genotype expressed 36 up-regulated and 21 down-regulated serine transcripts, respectively. Gene annotations for leaf samples were classified as having "molecular function" (46%), "cellular component" (19%), and "biological process" (35%), while root sample gene annotations were categorized as having "biological process" (573 contigs), "molecular function" (401 contigs), and "cellular components" (166 contigs). Noteworthy, polyamines play a crucial role in drought stress tolerance in the genotype, and it was found that top ten DEGs encoding for polyamines were common in two tissues (leaf and root). Collectively, transcriptomics profiling (RNA-seq) unveiled transcriptional stability drought stress provide a new insight in underlying modus of operandi in little millet genotype "OLM-203/Tarini" in response to heat stress.

Status of Universal Drug Susceptibility Testing in Pulmonary Tuberculosis Patients Initiated on Treatment in an Urban Setting, South India.

Introduction: Early initiation of drug susceptibility testing (DST) guided anti-tuberculosis treatment benefits the patient in terms of better treatment outcomes and possibly reduces the transmission of tuberculosis (TB) disease in the community. To determine the status of universal DST (UDST) coverage in smear-positive pulmonary TB patients (PTB) initiated on treatment under the TB program in Greater Chennai Corporation. In addition, the barriers and facilitators for UDST were explored. Material and Methods: The data of PTB patients who were initiated on anti-TB treatment from July to December 2019 was abstracted from the NI-KSHAY database of TB Program. The barriers and facilitators for UDST were explored in 5 focus group discussions (FGDs) among the TB program healthcare workers (HCW). UDST coverage was based on the availability of Cartridge-based Nucleic Acid Amplification test (CBNAAT) results in the NI-KSHAY database. Results: The CBNAAT result was available for 1628 (82.6%) of the 1970 smear-positive PTB patients. Non-availability of CBNAAT results was significantly higher among the older age group (>50 years), in female PTB patients, and the Private Sector. Issues with sputum collection, transport of specimens, and receipt of results were highlighted by the HCWs for the non-availability of UDST results. Conclusion: Universal DST coverage in smear-positive PTB patients initiated on treatment in 2019 in Chennai was optimal as per National Strategic Plan for TB elimination UDST target of 80% for the year 2020 but with scope for improvement. The low UDST coverage in the private sector, among female patients and older age groups, needs to be addressed.

ELUCNN for explainable COVID-19 diagnosis.

COVID-19 is a positive-sense single-stranded RNA virus caused by a strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several noteworthy variants of SARS-CoV-2 were declared by WHO as Alpha, Beta, Gamma, Delta, and Omicron. Till 13/Dec/2022, it has caused 6.65 million death tolls, and over 649 million confirmed positive cases. Based on the convolutional neural network (CNN), this study first proposes a ten-layer CNN as the backbone model. Then, the exponential linear unit (ELU) is introduced to replace ReLU, and the traditional convolutional block is now transformed into conv-ELU. Finally, an ELU-based CNN (ELUCNN) model is proposed for COVID-19 diagnosis. Besides, the MDA strategy is used to enhance the size of the training set. We develop a mobile app integrating ELUCNN, and this web app is run on a client-server modeled structure. Ten runs of the tenfold cross-validation experiment show our model yields a sensitivity of 94.41 ± 0.98 , a specificity of 94.84 ± 1.21 , an accuracy of 94.62 ± 0.96 , and an F1 score of 94.61 ± 0.95 . The ELUCNN model and mobile app are effective in COVID-19 diagnosis and give better results than 14 state-of-the-art COVID-19 diagnosis models concerning accuracy.

Prevalence of Entamoeba histolytica among schoolchildren in Dilla town, Gedeo zone, Ethiopia.

BACKGROUND: To measure the prevalence of Entamoeba histolytica (E. histolytica) infections and associated risk factors among schoolchildren in Dilla town, Gedeo zone Ethiopia. METHODS: A total of 501 schoolchildren were recruited by random sampling. Microscopically positive samples for E. histolytica cysts were further characterized using the E. histolytica II antigen detection kit. Univariate and multivariate logistic regression models were employed to analyze associations between E. histolytica infections, age, sex and study setting. RESULTS: Overall prevalence of E. histolytica was 13.17%. Hand washing before eating, hand washing after defecation, mother's occupation, and awareness about amebiasis were the factors associated with parasite prevalence. CONCLUSION: Prevalence of E. histolytica in the study population was high. Health promotion campaigns to create awareness about health and hygiene are clearly needed.

Automatic approach for mask detection: effective for COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in-person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a deep learning-based system that can detect instances of improper use of face masks. A dual-stage convolutional neural network architecture is used in our system to recognize masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. In this paper, we propose a variant of a multi-face detection model which has the potential to target and identify a group of people whether they are wearing masks or not.

LCCNN: a Lightweight Customized CNN-Based Distance Education App for COVID-19 Recognition.

In the global epidemic, distance learning occupies an increasingly important place in teaching and learning because of its great potential. This paper proposes a web-based app that includes a proposed 8-layered lightweight, customized convolutional neural network (LCCNN) for COVID-19 recognition. Five-channel data augmentation is proposed and used to help the model avoid overfitting. The LCCNN achieves an accuracy of 91.78%, which is higher than the other eight state-of-the-art methods. The results show that this web-based app provides a valuable diagnostic perspective on the patients and is an excellent way to facilitate medical education. Our LCCNN model is explainable for both radiologists and distance education users. Heat maps are generated where the lesions are clearly spotted. The LCCNN can detect from CT images the presence of lesions caused by COVID-19. This web-based app has a clear and simple interface, which is easy to use. With the help of this app, teachers can provide distance education and guide students clearly to understand the damage caused by COVID-19, which can increase interaction with students and stimulate their interest in learning.

Improving treatment adherence among tuberculosis patients through evening DOTS in Chennai, India.

Background Fixed days and timings of service are challenges in the care of patients with tuberculosis (TB). We assessed whether provision of evening DOTS (directly observed treatment, short course) improves treatment outcomes in a city with a working population. Methods We enrolled new adult patients with TB from seven tuberculous units (TUs) in this prospective cohort study. Participants were offered the option of DOTS during the day (8 a.m. to 3:30 p.m.) or evening (4 p.m. to 8 p.m.) and assigned accordingly. Results Of 127 patients enrolled between April and July 2017, 19 (15%) opted for evening DOTS. The number varied between the seven TUs (p=0.002). On an average, antitubercular therapy (ATT) was taken at 9:41 a.m. in the routine and 5:14 p.m. in the evening DOTS centres. Patients who were employed, left residence and returned back at 9:05 a.m. and 6:40 p.m., respectively. Around 96% (104/108) opted for day-time DOTS due to closeness of the centre to their residence. Around 74% (14/19) chose evening DOTS because of time convenience. Around 15% of patients on routine DOTS (16) had unfavourable treatment outcomes. All had favourable outcomes in the evening DOTS. Men were less likely and those withut alcohol disorders were more likely to have treatment success. Conclusion Provision of time convenient services might improve adherence and treatment outcome.

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm.

The majority of people in the modern biosphere struggle with depression as a result of the coronavirus pandemic's impact, which has adversely impacted mental health without warning. Even though the majority of individuals are still protected, it is crucial to check for post-corona virus symptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptoms and attacks that are present in the human body, the recommended approach is included. When a harmful virus spreads inside a human body, the post-diagnosis symptoms are considerably more dangerous, and if they are not recognised at an early stage, the risks will be increased. Additionally, if the post-symptoms are severe and go untreated, it might harm one's mental health. In order to prevent someone from succumbing to depression, the technology of audio prediction is employed to recognise all the symptoms and potentially dangerous signs. Different choral characters are used to combine machine-learning algorithms to determine each person's mental state. Design considerations are made for a separate device that detects audio attribute outputs in order to evaluate the effectiveness of the suggested technique; compared to the previous method, the performance metric is substantially better by roughly 67%.

A Seven-Layer Convolutional Neural Network for Chest CT-Based COVID-19 Diagnosis Using Stochastic Pooling.

(Aim) COVID-19 pandemic causes numerous death tolls till now. Chest CT is an effective imaging sensor system to make accurate diagnosis. (Method) This article proposed a novel seven layer convolutional neural network based smart diagnosis model for COVID-19 diagnosis (7L-CNN-CD). We proposed a 14-way data augmentation to enhance the training set, and introduced stochastic pooling to replace traditional pooling methods. (Results) The 10 runs of 10-fold cross validation experiment show that our 7L-CNN-CD approach achieves a sensitivity of 94.44±0.73, a specificity of 93.63±1.60, and an accuracy of 94.03±0.80. (Conclusion) Our proposed 7L-CNN-CD is effective in diagnosing COVID-19 in chest CT images. It gives better performance than several state-of-the-art algorithms. The data augmentation and stochastic pooling methods are proven to be effective.

Predicting occupational injury causal factors using text-based analytics: A systematic review.

Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research.

Prevalence and associated risk factors of Entamoeba histolytica infection among school children from three primary schools in Arsi Town, West Zone, Ethiopia.

Entamoeba histolytica is one of the major pathogenic intestinal parasites and is amongst the leading causes of diarrheal illness in children. Around 500 million people are infected worldwide, while 75,000 die of the disease annually. E. histolytica is associated with moderate to severe diarrhoea and increased mortality among children in African countries and negatively affects child growth and development. Malnutrition is also an important contributor to the prevalence of E. histolytica in Ethiopia. It is mostly associated with poor hygiene, poverty, illiteracy, lack of access to potable water, and a hot, humid tropical climate. Thus, the present investigation was aimed to assess the prevalence rate and associated factors of E. histolytica among schoolchildren in Arsi Town, West Zone, Oromia Regional State, Ethiopia. A cross-sectional study was conducted from February 1st to May 1st, 2020. A total number of 334 students were selected from three governmental elementary schools in the west zone using a systematic sampling method. A structured questionnaire was adopted to identify environmental, socio-demographic, and behavioral factors. Microscopically positive samples for E. histolytica cysts were further characterised using an E. histolytica II antigen detection kit. The statistical analysis of the data was done using SPSS software. A univariate and multivariate logistic regression analysis was done. P value < 0.05 was considered statistically significant. The overall prevalence of E. histolytica was 19.8%. Hand washing habits before eating [AOR = 0.32; 95% CI (0.12, 0.84)], hand washing habits after defecation [AOR = 0.396; 95% CI (0.165, 0.946)], and ameba awareness [AOR = 0.35; 95% CI (0.142, 0.889)] were factors associated with parasite prevalence. The findings of this study could assist the government in targeting infected areas, improving sanitation to prevent E. histolytica transmission, and implementing effective control measures in these rural communities, particularly among youngsters, who represent the nation's future.

Pharmaceutical interventions: A solution to stop smoking.

Background: Smoking has become a worldwide problem and the more problematic is when the youth of any country is highly involved in consuming tobacco that is dangerous for the human being. The Indian youth is involved in smoking-like activities that are dangerous for them. Objectives: To analyze the role of new treatment ways including nicotine gum, nicotine patch, and nicotine replacement therapy backed by pharmaceutical interventions to eliminate smoking from Indian society. Method: The hypotheses of this study are formed on the basis of previous studies and the identification of gap in practices. This study is based on the cross-sectional data that was collected with the help of a questionnaire based on a five-point Likert scale from the Indian pharma industry people. The sample size for this study was 340 with a response rate of 50%. Results: In this study, 179 valid questionnaires were collected back from the respondents, and according to the results, nicotine gum, nicotine patch, and nicotine replacement therapies have a significant (p<0.05) effect on smoker attitudes. Conclusion: This study concludes that pharmaceutical interventions are critical to stopping the smoking habit in Indian society, and this contribution is providing a solution to stop smoking by affecting the smoker's attitude and smoking behavior.

Psychological distress and burnout among healthcare worker during COVID-19 pandemic in India-A cross-sectional study.

BACKGROUND: COVID-19 has inundated the entire world disrupting the lives of millions of people. The pandemic has stressed the healthcare system of India impacting the psychological status and functioning of health care workers. The aim of this study is to determine the burnout levels and factors associated with the risk of psychological distress among healthcare workers (HCW) engaged in the management of COVID 19 in India. METHODS: A cross-sectional study was conducted from 1 September 2020 to 30 November 2020 by telephonic interviews using a web-based Google form. Health facilities and community centres from 12 cities located in 10 states were selected for data collection. Data on socio-demographic and occupation-related variables like age, sex, type of family, income, type of occupation, hours of work and income were obtained was obtained from 967 participants, including doctors, nurses, ambulance drivers, emergency response teams, lab personnel, and others directly involved in COVID 19 patient care. Levels of psychological distress was assessed by the General health Questionnaire -GHQ-5 and levels of burnout was assessed using the ICMR-NIOH Burnout questionnaire. Multivariable logistic regression analysis was performed to identify factors associated with the risk of psychological distress. The third quartile values of the three subscales of burnout viz EE, DP and PA were used to identify burnout profiles of the healthcare workers. RESULTS: Overall, 52.9% of the participants had the risk of psychological distress that needed further evaluation. Risk of psychological distress was significantly associated with longer hours of work (≥ 8 hours a day) (AOR = 2.38, 95% CI(1.66-3.41), income≥20000(AOR = 1.74, 95% CI, (1.16-2.6); screening of COVID-19 patients (AOR = 1.63 95% CI (1.09-2.46), contact tracing (AOR = 2.05, 95% CI (1.1-3.81), High Emotional exhaustion score (EE ≥16) (AOR = 4.41 95% CI (3.14-6.28) and High Depersonalisation score (DP≥7) (AOR = 1.79, 95% CI (1.28-2.51)). About 4.7% of the HCWs were overextended (EE>18); 6.5% were disengaged (DP>8) and 9.7% HCWs were showing signs of burnout (high on all three dimensions). CONCLUSION: The study has identified key factors that could have been likely triggers for psychological distress among healthcare workers who were engaged in management of COVID cases in India. The study also demonstrates the use of GHQ-5 and ICMR-NIOH Burnout questionnaire as important tools to identify persons at risk of psychological distress and occurrence of burnout symptoms respectively. The findings provide useful guide to planning interventions to mitigate mental health problems among HCW in future epidemic/pandemic scenarios in the country.

Stability analysis based parameter tuning of Social Group Optimization.

Swarm-based optimization algorithms have been popularly used these days for optimization of various real world problems but sometimes it becomes hard to estimate the associated characteristics due to their stochastic nature. To ensure a steady performance of these techniques, it is essential to have knowledge about the range of variables, in which a particular algorithm always provides stable performance and performing stability analysis of an algorithm can help in providing some knowledge regarding the same. Many researchers have performed the stability analysis of several optimization algorithms and analyzed their behavior. Social Group Optimization (SGO) is a newly developed algorithm which has been proven to yield promising results when applied to many real world problems but in literature no work can be found on stability analysis of SGO. In this paper, Von Neumann stability analysis approach has been used for performing stability analysis of Social Group Optimization (SGO) to analyze the behavior of its algorithmic parameters and estimate the range in which they always give stable convergence. The results obtained have been supported by sufficient experimental analysis and simulated using eight benchmark function suite along with their shifted and rotated variations which prove that the algorithm performs better within the stable range and hence convergence is ensured.