New Way of Identification of a Place and Tracking

Identification of a place and navigation to reach are two most important things for any traveler. Although Google map has been helping the society at large in many ways, it has some disadvantages. For example, all the postal addresses cannot be identifiable through Google map APP. There is no unique place for identification as popular name of a location has several places. Additionally, it depends wholly on GPS accuracy and may sometimes be away from the desired location by 100 meters. Some of these disadvantages are overcome from our new way of identification of a place. Our innovation is simple but its applications are many. We can provide code for any place on the land, water or ice-covered surface of this planet with 8-digit alphanumeric code (TH code). This code is integrated with Google map and implemented in Android based mobile phones and can easily be extended to IOS based Apple mobile phones as well. The accuracy of our code location is about one meter anywhere in the world. To get the code of a location, GPS is not required but internet service is necessary. However, to navigate from one place to the other both GPS and Internet are required. Our APP is quite simple to operate and useful to many and has applications at least in ten different sectors. In this present-day Corona virus scenario, our APP is vital to track human beings, goods, medical equipment etc. to reduce human loss, economy loss due to quarantine/lockdown issues and it is the need of the hour.

Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

Rooftop units(RTUs)were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive.Fault detection and diagnosis(FDD)tools can be employed for RTU methods to ensure essential faults are addressed promptly.In this aspect,this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units(ODBNFDC-PRTU)model.The ODBNFDC-PRTU technique considers fault diagnosis as amulti-class classification problem and is handled usingDL models.For fault diagnosis in RTUs,the ODBNFDC-PRTU model exploits the deep belief network(DBN)classification model,which identifies seven distinct types of faults.At the same time,the chicken swarm optimization(CSO)algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process,showing the novelty of the work.To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm,a comprehensive set of simulations are applied.The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30%and TPR of 93.09%.

Phytoconstituents Profiling of Indigenous Herbal Drugs and Its in vitro Microbial,in Silico Biological Examination against Severe Acute Respiratory Syndrome Coronavirus 2

Background:Habb-E-Shifa,Hamdard Sualin,and Hamdard Joshanda traditional herbal medicines may promote host resistance against infection by bacteria,viruses,and fungi which are easily accessible at inexpensive with no complexity.These herbal medicines are used to cure sore throat,cough,fever,lung cancer,and asthma patients in developing South Asian countries.These traditional herbal medicines acted a crucial role in the prevention and control of coronavirus disease 2019(COVID-19).Aims and Objectives:This research article aimed at conducting phytochemistry,antimicrobial activity,COVID-19 docking and some spectroscopic(Infrared,Ultraviolet,13C-Nuclear Magnetic Resonance(13C-NMR),1H-NMR,and Mass Spectra)characterizations of the polyherbal drugs were carried out.Additionally,In-vitro and In-silico analyses were performed to measure activity against COVID-19.High Performance-Liquid Chromatography(HPLC),Gas Chromatography-Mass Spectrometry(GC-MS),antimicrobial,and docking studies were carried out.The preliminary phytochemical assay and bioactive compounds were screened using HPLC and GC-MS.The study is an attempt to assess the promising effects of selected polyherbal indigenous drugs such as Habb-E-Shifa,Hamdard Sualin,and Hamdard Joshanda phytoconstituents against the severe acute respiratory syndrome coronavirus-2(SARS-Co V-2).Materials and Methods:The extract of the selected polyherbal formulations showed high-to-moderate preventive effects on the growth inhibition in the pathogenic bacterium,namely Streptococcus oralis,Staphylococcus aureus,Propionibacterium acnes,Pseudomonas aeruginosa,Escherichia coli,and Proteus vulgaris,and three fungal Candida albicans,Aspergillus fumigatus,and Aspergillus niger.Further docking study evaluates the pharmacological activity of bioactive chemical compounds with SARS-Co V-2 NSP5(PDB ID:7nxh)and SARS-Co V-2 Omicron spike protein with human angiotensin-converting enzyme 2(ACE-2)(PDB ID:7wk6).Results:In this study,for the first time,we attempted to examine some spectroscopic characterization of selected herbals.The total phenol content(1.66,1.55,and 1.13 mg/m L)and total flavonoid content(4.92,0.49,and 0.50 mg/m L)were present in the extracted samples of Habb-E-Shifa(H),Hamdard Joshanda(J),and Hamdard Sualin(S).Studies on COVID-19 docking infer the affinity of the herb's chemical components toward COVID-19 protease and ACE-2 receptor by establishing excellent binding capacity in complex formation.The results confirmed that polyherbal drugs harbor biological activities and thereby highlight that these extracts can serve as a remedy for antimicrobial and COVID-19.Conclusions:The research article confirms the remarkable potential in exhibiting antimicrobial activity against Gram-positive,Gram-negative bacteria and fungi.These herbal medicines such as Habb-E-Shifa(H),Hamdard Joshanda(J),and Hamdard Sualin(S)showed a vital role against SARS-Co V-2 Omicron spike protein with human ACE2(7wk6)and amino acids of SARS-Co V-2 NSP5(7nxh).Our study provides obvious evidence supporting dietary therapy and herbal medicine as potentially effective against SARS-CoV-2.Based on present studies,these herbal products can be introduced as preventive and therapeutic agents fight against coronavirus.

Colorectal carcinogenesis:Insights into the cell death and signal transduction pathways:A review

Colorectal carcinogenesis(CRC) imposes a major health burden in developing countries. It is the third major cause of cancer deaths. Despite several treatment strategies, novel drugs are warranted to reduce the severity of this disease. Adenomatous polyps in the colon are the major culprits in CRC and found in 45% of cancers, especially in patients 60 years of age. Inflammatory polyps are currently gaining attention in CRC, and a growing body of evidence denotes the role of inflammation in CRC. Several experimental models are being employed to investigate CRC in animals, which include the APC^(min/+) mouse model, Azoxymethane, Dimethyl hydrazine, and a combination of Dextran sodium sulphate and dimethyl hydrazine. During CRC progression, several signal transduction pathways are activated. Among the major signal transduction pathways are p53, Transforming growth factor beta, Wnt/β-catenin, Delta Notch, Hippo signalling, nuclear factor erythroid 2-related factor 2 and Kelch-like ECH-associated protein 1 pathways. These signalling pathways collaborate with cell death mechanisms, which include apoptosis, necroptosis and autophagy, to determine cell fate. Extensive research has been carried out in our laboratory to investigate these signal transduction and cell death mechanistic pathways in CRC. This review summarizes CRC pathogenesis and the related cell death and signal transduction pathways.

Dye-tolerant marine Acinetobacter baumannii-mediated biodegradation of reactive red

The objective of this study was to isolate a potent dye-degrading microbe that can be used to reduce the pollution caused by industrial dyes.Reactive red 198 is an extensively used textile dye and is a major environmental pollutant in water bodies. In this study, a bacterial strain was isolated from sea sediments and identified as Acinetobacter baumannii with 16S rRNA sequencing. The isolated bacteria were immobilized in calcium alginate and decolorization studies were carried out to determine the optimum pH, temperature, dye concentration, inoculum volume,and static/agitated condition using the one factor at a time(OFAT) approach. The Box-Behnken design, a type of response surface methodology,was adopted to improve the degradation efficiency. At 37℃ using an inoculum volume of six beads, 96.20% decolorization was observed in 500 mg/L of reactive red 198 after 72 h. Dye degradation was confirmed with UV-visible spectroscopy and Fourier-transform infrared(FTIR)spectroscopy studies of the dye and degraded metabolites. Microbial toxicity studies using Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa and phytotoxicity studies using Vigna radiata proved that the toxicity of the dye was significantly reduced after degradation. We can conclude that the isolated A. baumannii strain is an efficient dye-degrading microbe that can be used to reduce the pollution caused by industrial dyes.

An Investigation of Molecular Targeting of MMP-9 for Endometriosis Using Algal Bioactive Molecules

Endometriosis is a chronic inflammatory disease that occurs due to the presence of endometrial tissue outside the uterine cavity.It affects from 5%to 10%of women of reproductive age.High levels of matrix metalloproteinase(especially MMP-9)have been observed in women suffering from endometriosis.Thus,the aim of this study was to investigate the naturally anti-inflammatory compounds available from an algal source that can target the MMP-9 by various in silico approaches.The target 1L6J(Crystal structure of human matrix metalloproteinase MMP-9)structure was retrieved from the PDB database.Five compounds such as Eckol,Sargafuran,Vitamin E,Docosahexaenoic acid,Fucoidan and Elagolix were selected based on‘Lipinski’s rule of five’using the PubChem database.The pharmacokinetics,ADMET properties and biological activity of these compounds were predicted computationally using databases such as PreADME,SWISS-ADME,pkCSM and PASS.Comparative analysis of the bioactive compounds with the target was performed by AutoDock 4.2.6.Using LigPlot v.2.2,the target residues interacting with the compounds were visualised in a 2D manner.Based on the results,Eckol exhibited the highest binding energy value of−7.82 kcal/mol,whereas the Elagolix(control drug)showed a binding energy of−4.88 kcal.We conclude that Eckol can be a potent inhibitor of target MMP-9 with least side effects when compared to the control drug.Hence,this compound can be effectively explored by further in vitro and in vivo studies to develop more effective treatments for Endometriosis.

IoT Information Status Using Data Fusion and Feature Extraction Method

The Internet of Things(IoT)role is instrumental in the technological advancement of the healthcare industry.Both the hardware and the core level of software platforms are the progress resulted from the accompaniment of Medicine 4.0.Healthcare IoT systems are the emergence of this foresight.The communication systems between the sensing nodes and the processors;and the processing algorithms to produce output obtained from the data collected by the sensors are the major empowering technologies.At present,many new technologies supplement these empowering technologies.So,in this research work,a practical feature extraction and classification technique is suggested for handling data acquisition besides data fusion to enhance treatment-related data.In the initial stage,IoT devices are gathered and pre-processed for fusion processing.Dynamic Bayesian Network is considered an improved balance for tractability,a tool for CDF operations.Improved Principal Component Analysis is deployed for feature extraction along with dimension reduction.Lastly,this data learning is attained through Hybrid Learning Classifier Model for data fusion performance examination.In this research,Deep Belief Neural Network and Support VectorMachine are hybridized for healthcare data prediction.Thus,the suggested system is probably a beneficial decision support tool for multiple data sources prediction and predictive ability enhancement.

Investigating seasonality,policy intervention and forecasting in the Indian gold futures market:a comparison based on modeling non‑constant variance using two different methods

This study focuses on the Indian gold futures market where primary participants hold sentimental value for the underlying asset and are globally ranked number two in terms of the largest private holdings in the physical form.The trade of gold futures relates to seasons,festivity,and government policy.So,the paper will discuss seasonality and intervention in the analysis.Due to non-constant variance,we will also use the standard variance stabilization transformation method and the ARIMA/GARCH modelling method to compare the forecast performance on the gold futures prices.The results from the analysis show that while the standard variance transformation method may provide better point forecast values,the ARIMA/GARCH modelling method provides much shorter forecast intervals.The empirical results of this study which rationalise the effect of seasonality in the Indian bullion derivative market have not been reported in literature.

Forecasting the Municipal Solid Waste Using GSO-XGBoost Model

Waste production rises in tandem with population growth and increased utilization.The indecorous disposal of waste paves the way for huge disaster named as climate change.The National Environment Agency(NEA)of Singapore oversees the sustainable management of waste across the country.The three main contributors to the solid waste of Singapore are paper and cardboard(P&C),plastic,and food scraps.Besides,they have a negligible rate of recycling.In this study,Machine Learning techniques were utilized to forecast the amount of garbage also known as waste audits.The waste audit would aid the authorities to plan their waste infrastructure.The applied models were k-nearest neighbors,Support Vector Regressor,ExtraTrees,CatBoost,and XGBoost.The XGBoost model with its default parameters performed better with a lower Mean Absolute Percentage Error(MAPE)of 8.3093(P&C waste),8.3217(plastic waste),and 6.9495(food waste).However,Grid Search Optimization(GSO)was used to enhance the parameters of the XGBoost model,increasing its effectiveness.Therefore,the optimized XGBoost algorithm performs the best for P&C,plastics,and food waste with MAPE of 4.9349,6.7967,and 5.9626,respectively.The proposed GSO-XGBoost model yields better results than the other employed models in predicting municipal solid waste.

硬毛巴豆减弱链脲佐菌素诱导大鼠的神经炎症

目的本研究旨在探讨硬毛巴豆(CH)提取物在链脲佐菌素(STZ)大鼠中的神经保护作用。方法(1)亚慢性毒性试验包括24只成年大鼠,雌雄不限,体重160~200 g,分为4组,每组6只。第1组大鼠给予二甲基亚砜(DMSO)与生理盐水混合物;第2、3、4组大鼠灌胃给药硬毛巴豆甲醇提取物(MECH),剂量分别为100、200和400 mg/kg,连续28天。将功能观察组合和自发活动分级作为其神经行为活动的一部分,并对脑区(如皮质和海马)进行神经病理学异常分析。(2)研究动物体重为160~200 g的成年雄性Wistar大鼠,分为5组,每组6只,包括对照组(Ⅰ)、STZ组(Ⅱ)、多奈哌齐组(Ⅲ)、MECH组(100 mg/kg,Ⅳ)和MECH组(200 mg/kg,Ⅴ)。第Ⅰ组大鼠口服柠檬酸盐缓冲液和DMSO生理盐水混合物(14天)。第Ⅱ组大鼠在第1天和第3天侧脑室注射链脲佐菌素(3 mg/kg,双侧ICV-STZ),随后给予生理盐水DMSO混合物(14天)。第Ⅲ、Ⅳ和Ⅴ组大鼠在第1天和第3天给予STZ,随后口服多奈哌齐[3 mg/(kg·d)]和MECH[100和200 mg/(kg·d)](14天)。检测大鼠Morris水迷宫(MWM)和被动回避试验(PAT)等学习记忆指标。完成行为学实验后处死动物,取脑,用紫外可见分光光度计测定乙酰胆碱酯酶(AChE)、脂质过氧化(LPO)、谷胱甘肽(GSH)和超氧化物歧化酶(SOD)水平。使用总荧光发射光谱法测定caspase-3水平。通过酶联免疫吸附测定(ELISA)检测β淀粉样蛋白(Aβ)水平,同时对CA1海马区进行组织病理学分析。结果(1)亚慢性毒性结果显示,各组MECH大鼠的旷场试验参数(包括线交叉、直立、进入中广场、排便或排尿)无显著差异(P>0.05)。组织病理学结果显示,与对照组大鼠相比,经过MECH治疗的大鼠两个区域的神经元细胞中均未产生任何病变和炎症。(2)研究主要发现STZ处理大鼠在学习和记忆参数受损显著增加(P<0.001),AChE、caspase-3、Aβ(1-40和1-42)和LPO水平均显著升高(P<0.001),且SOD和GSH水平显著降低(P<0.01)。此外,在海马区发现神经元损伤。相反,STZ诱导大鼠的行为和生化损伤与MECH治疗呈剂量依赖性降低。结论MECH通过其抗氧化作用可显著预防STZ诱导的记忆缺陷,胆碱能功能的恢复可能是行为改善的原因。因此,MECH可能治疗包括阿尔茨海默病在内的认知障碍。

Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation

The diagnostic interpretation of dermoscopic images is a complex task as it is very difficult to identify the skin lesions from the normal.Thus the accurate detection of potential abnormalities is required for patient monitoring and effec-tive treatment.In this work,a Two-Tier Segmentation(TTS)system is designed,which combines the unsupervised and supervised techniques for skin lesion seg-mentation.It comprises preprocessing by the medianfilter,TTS by Colour K-Means Clustering(CKMC)for initial segmentation and Faster Region based Con-volutional Neural Network(FR-CNN)for refined segmentation.The CKMC approach is evaluated using the different number of clusters(k=3,5,7,and 9).An inception network with batch normalization is employed to segment mel-anoma regions effectively.Different loss functions such as Mean Absolute Error(MAE),Cross Entropy Loss(CEL),and Dice Loss(DL)are utilized for perfor-mance evaluation of the TTS system.The anchor box technique is employed to detect the melanoma region effectively.The TTS system is evaluated using 200 dermoscopic images from the PH2 database.The segmentation accuracies are analyzed in terms of Pixel Accuracy(PA)and Jaccard Index(JI).Results show that the TTS system has 90.19%PA with 0.8048 JI for skin lesion segmentation using DL in FR-CNN with seven clusters in CKMC than CEL and MAE.

Power Quality Improvement Using ANN Controller For Hybrid Power Distribution Systems

In this work,an Artificial Neural Network(ANN)based technique is suggested for classifying the faults which occur in hybrid power distribution systems.Power,which is generated by the solar and wind energy-based hybrid system,is given to the grid at the Point of Common Coupling(PCC).A boost converter along with perturb and observe(P&O)algorithm is utilized in this system to obtain a constant link voltage.In contrast,the link voltage of the wind energy conversion system(WECS)is retained with the assistance of a Proportional Integral(PI)controller.The grid synchronization is tainted with the assis-tance of the d-q theory.For the analysis of faults like islanding,line-ground,and line-line fault,the ANN is utilized.The voltage signal is observed at the PCC,and the Discrete Wavelet Transform(DWT)is employed to obtain different features.Based on the collected features,the ANN classifies the faults in an effi-cient manner.The simulation is done in MATLAB and the results are also validated through the hardware implementation.Detailed fault analysis is carried out and the results are compared with the existing techniques.Finally,the Total harmonic distortion(THD)is lessened by 4.3%by using the proposed methodology.

Analysis of Protein-Ligand Interactions of SARS-CoV-2 Against Selective Drug Using Deep Neural Networks

In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems. The machine learning methods greatly offer an efficient prediction ability in diagnosis system alternative with the clinicians. Most of the systems operate on the extracted features from the patients and most of the predicted cases are accurate. However, in recent time, the prevalence of COVID-19 has emerged the global healthcare industry to find a new drug that suppresses the pandemic outbreak. In this paper, we design a Deep Neural Network(DNN)model that accurately finds the protein-ligand interactions with the drug used. The DNN senses the response of protein-ligand interactions for a specific drug and identifies which drug makes the interaction that combats effectively the virus. With limited genome sequence of Indian patients submitted to the GISAID database, we find that the DNN system is effective in identifying the protein-ligand interactions for a specific drug.

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction(MOMPSO)

Software testing plays a pivotal role in entire software development lifecycle.It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required.These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite,fault-tolerance,defects due to uncovered-statements and overall-performance at the time of execution.With this objective,the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization.The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate(90.78%to 100%)and failure detection rate(44.56%to 55.01%).Results proved MOMPSO outperformed the other stated algorithms.