The novelty of this research lies in the successful fabrication of a 3D-printed honeycomb structure filled with nanofillers for acoustic properties,utilizing an impedance tube setup in accordance with ASTM standard E ...The novelty of this research lies in the successful fabrication of a 3D-printed honeycomb structure filled with nanofillers for acoustic properties,utilizing an impedance tube setup in accordance with ASTM standard E 1050-12.The Creality Ender-3,a 3D printer,was used for printing the honeycomb structures,and polylactic acid(PLA)material was employed for their construction.The organic,inorganic,and polymeric compounds within the composites were identified using fourier transformation infrared(FTIR)spectroscopy.The structure and homogeneity of the samples were examined using a field emission scanning electron microscope(FESEM).To determine the sound absorption coefficient of the 3D printed honeycomb structure,numerous samples were systematically developed using central composite design(CCD)and analysed using response surface methodology(RSM).The RSM mathematical model was established to predict the optimum values of each factor and noise reduction coefficient(NRC).The optimum values for an NRC of 0.377 were found to be 1.116 wt% carbon black,1.025 wt% aluminium powder,and 3.151 mm distance between parallel edges.Overall,the results demonstrate that a 3Dprinted honeycomb structure filled with nanofillers is an excellent material that can be utilized in various fields,including defence and aviation,where lightweight and acoustic properties are of great importance.展开更多
Whole grains of proso and barnyard millets were sequentially extracted using different solvents(hexane,chloroform,ethyl acetate,and methanol).Phytochemical analysis was performed qualitatively,and the total phenolic c...Whole grains of proso and barnyard millets were sequentially extracted using different solvents(hexane,chloroform,ethyl acetate,and methanol).Phytochemical analysis was performed qualitatively,and the total phenolic content in the extracts of proso and barnyard millets was quantified.Alkaloids and cardiac glycosides were identified in all solvent extracts of both millets.Anthraquinone and glycosides yielded negative results in all solvent extracts of both millets.Among all the solvent extracts,methanol extracts of proso and barnyard millets showed the presence of major compounds such as flavonoids,terpenoids,amino acids,tannins,and phenolics compounds.The maximum amount of phenols was found in methanolic extracts of proso and barnyard millets(0.669±0.003 and 0.625±0.003),followed by the chloroform extract of proso and barnyard millets(0.284±0.002 and 0.257±0.003).The minimum amount of phenolics was found in the acetone extract of proso and barnyard millets.The methanol extract of both millets showed the presence of major compounds with high phenolic content.展开更多
Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronar...Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.展开更多
Fuel cells have attracted extensive attention due to their high conversion efficiency and environmental friendliness.However,their wider application is limited by the poor activity and high cost of platinum(Pt),which ...Fuel cells have attracted extensive attention due to their high conversion efficiency and environmental friendliness.However,their wider application is limited by the poor activity and high cost of platinum(Pt),which is widely used as the cathode catalyst to overcome the slow kinetics associated with oxygen reduction reaction(ORR).Pt‐based composites with one‐dimensional(1D)nanoarchitectures demonstrate great advantages towards efficient ORR catalysis.This review focuses on the recent advancements in the design and synthesis of 1D Pt‐based ORR catalysts.After introducing the fundamental ORR mechanism and the advanced 1D architectures,their synthesis strategies(template‐based and template‐free methods)are discoursed.Subsequently,their morphology and structure optimization are highlighted,followed by the superstructure assembly using 1D Pt‐based blocks.Finally,the challenges and perspectives on the synthesis innovation,structure design,physical characterization,and theoretical investigations are proposed for 1D Pt‐based ORR nanocatalysts.We anticipate this study will inspire more research endeavors on efficient ORR nanocatalysts in fuel cell application.展开更多
We show the results of first-principles calculations of structural,phonon,elastic,thermal and electronic properties of the Mg-X inter-metallics in their respective ground state phase and meta-stable phases at equilibr...We show the results of first-principles calculations of structural,phonon,elastic,thermal and electronic properties of the Mg-X inter-metallics in their respective ground state phase and meta-stable phases at equilibrium geometry and the studied pressure range.Phonon dispersion spectra for these compounds were investigated by using the linear response technique.The phonon spectra do not show any abnormality in their respective ground state phase.The respective ground states phases of the studied system remain stable within the studied pressure range.Electronic and thermodynamic properties were derived by analysis of the electronic structures and quasi-harmonic approximation.The mixed bonding character of the Mg-X intermetallics is revealed by Mg-X bonds,and it leads the metallic nature.Most of the contribution originated from X ions d like states at Fermi level compared to that of Mg ion in these intermetallics.In this work,we also predicted the melting temperature of these intermetallics and evaluated the Debye temperature by using elastic constants.展开更多
Bridging the performance gap of the electrocatalyst between the rotating disk electrode(RDE) and membrane electrode assembly(MEA) level testing is the key to reducing the total cost of proton exchange membrane fuel ce...Bridging the performance gap of the electrocatalyst between the rotating disk electrode(RDE) and membrane electrode assembly(MEA) level testing is the key to reducing the total cost of proton exchange membrane fuel cell(PEMFC) vehicles. Presently, platinum metal accounts for ~42% of the total cost of the PEMFC vehicles for usage in the cathode catalyst layer, where the sluggish oxygen reduction reaction(ORR) occurs. An alternative to the platinum catalyst, the Fe-N-C catalyst has attracted considerable interest for PEMFC due to its cost-effectiveness and high catalytic activity towards ORR. However, the excellent ORR activity of Fe-N-C obtained from RDE studies rarely translates the same performance into MEA operating conditions. Such a performance gap is mainly attributed to the lack of atomic-level understanding of Fe-N-C active sites and their ORR mechanism. Besides, unless the cost of expensive electrocatalyst is reduced, the total operation cost of the PEMFC vehicles remains constant. Therefore,developing highly efficient Fe-N-C catalysts from academic and industrial perspectives is critical for commercializing PEMFC vehicles. Here, the scope of the review is three-fold. First, we discussed the atomiclevel insights of Fe-N-C active sites and ORR mechanism, followed by unraveling the different iron-based nanostructured ORR electrocatalysts, including oxide, carbide, nitride, phosphide, sulfide, and singleatom catalysts. And then we bridged their ORR catalytic performance gap between the RDE and MEA tests for real operating conditions of PEMFC vehicles. Second, we focused on bridging the cost barriers of PEMFC vehicles between capital, operation, and end-user. Finally, we provided the path to achieve sustainable development goals by commercializing PEMFC vehicles for a better world.展开更多
Android devices are popularly available in the commercial market at different price levels for various levels of customers.The Android stack is more vulnerable compared to other platforms because of its open-source na...Android devices are popularly available in the commercial market at different price levels for various levels of customers.The Android stack is more vulnerable compared to other platforms because of its open-source nature.There are many android malware detection techniques available to exploit the source code andfind associated components during execution time.To obtain a better result we create a hybrid technique merging static and dynamic processes.In this paper,in thefirst part,we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Mul-ticollinearity problem is one of the drawbacks in the existing system.In the proposed work,a novel PCA(Principal Component Analysis)based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given approach.The Android Sensitive Permission is one major key point to be considered while detecting malware.We select vulnerable columns based on features like sensitive permissions,application program interface calls,services requested through the kernel,and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or benign.Thefinal goal of this paper is to check benchmarking datasets collected from various repositories like virus share,Github,and the Canadian Institute of cyber security,compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.展开更多
Purpose:The aim of this study was to review,systematically,evidence concerning the link between the ACTN3 R577X polymorphism and the rates and severity of non-contact injuries and exercise-induced muscle damage in ath...Purpose:The aim of this study was to review,systematically,evidence concerning the link between the ACTN3 R577X polymorphism and the rates and severity of non-contact injuries and exercise-induced muscle damage in athletes and individuals enrolled in exercise training programs.Methods:A computerized literature search was performed in the electronic databases PubMed,Web of Science,and SPORTDiscus,from inception until November 2020.All included studies compared the epidemiological characteristics of non-contact injury between the different genotypes of the ACTN3 R577X polymorphism.Results:Our search identified 492 records.After the screening of titles,abstracts,and full texts,13 studies examining the association between the ACTN3 genotypes and the rate and severity of non-contact injury were included in the analysis.These studies were performed in 6 different countries(Spain,Japan,Brazil,China,the Republic of Korea,and Italy)and involved a total participant pool of 1093 participants.Of the studies,2 studies involved only women,5 studies involved only men,and 6 studies involved both men and women.All the studies included were classified as highquality studies(≥6 points in the Physiotherapy Evidence Database(PEDro)scale score).Overall,evidence suggests there is an association between the ACTN3 R577X genotype and non-contact injury in 12 investigations.Six studies observed a significant association between A CTN3 R577X polymorphism and exercise induced muscle damage:2 with non-contact ankle injury,3 with non-contact muscle injury,and 1 with overall non-contact injury.Conclusion:The present findings support the premise that possessing the ACTN3 XX genotype may predispose athletes to a higher probability of some non-contact injuries,such as muscle injury,ankle sprains,and higher levels of exercise-induced muscle damage.展开更多
Mesenchymal stem cells(MSCs)originate from many sources,including the bone marrow and adipose tissue,and differentiate into various cell types,such as osteoblasts and adipocytes.Recent studies on MSCs have revealed th...Mesenchymal stem cells(MSCs)originate from many sources,including the bone marrow and adipose tissue,and differentiate into various cell types,such as osteoblasts and adipocytes.Recent studies on MSCs have revealed that many transcription factors and signaling pathways control osteogenic development.Osteogenesis is the process by which new bones are formed;it also aids in bone remodeling.Wnt/β-catenin and bone morphogenetic protein(BMP)signaling pathways are involved in many cellular processes and considered to be essential for life.Wnt/β-catenin and BMPs are important for bone formation in mammalian development and various regulatory activities in the body.Recent studies have indicated that these two signaling pathways contribute to osteogenic differen-tiation.Active Wnt signaling pathway promotes osteogenesis by activating the downstream targets of the BMP signaling pathway.Here,we briefly review the molecular processes underlying the crosstalk between these two pathways and explain their participation in osteogenic differentiation,emphasizing the canonical pathways.This review also discusses the crosstalk mechanisms of Wnt/BMP signaling with Notch-and extracellular-regulated kinases in osteogenic differentiation and bone development.展开更多
The concept of inflammatory bowel disease(IBD),which encompasses Crohn’s disease and ulcerative colitis,represents a complex and growing global health concern resulting from a multifactorial etiology.Both dysfunction...The concept of inflammatory bowel disease(IBD),which encompasses Crohn’s disease and ulcerative colitis,represents a complex and growing global health concern resulting from a multifactorial etiology.Both dysfunctional autophagy and dysbiosis contribute to IBD,with their combined effects exacerbating the related inflammatory condition.As a result,the existing interconnection between gut microbiota,autophagy,and the host’s immune system is a decisive factor in the occurrence of IBD.The factors that influence the gut microbiota and their impact are another important point in this regard.Based on this initial perspective,this manuscript briefly highlighted the intricate interplay between the gut microbiota,autophagy,and IBD pathogenesis.In addition,it also addressed the potential targeting of the microbiota and modulating autophagic pathways for IBD therapy and proposed suggestions for future research within a more specific and expanded context.Further studies are warranted to explore restoring microbial balance and regulating autophagy mechanisms,which may offer new therapeutic avenues for IBD management and to delve into personalized treatment to alleviate the related burden.展开更多
MicroRNAs(miRNAs)are small non-coding RNAs(ncRNAs)that regulate the expression of their targetmRNAs post-transcriptionally.Since their discovery,thousands of highly conserved miRNAs have been identified and investigat...MicroRNAs(miRNAs)are small non-coding RNAs(ncRNAs)that regulate the expression of their targetmRNAs post-transcriptionally.Since their discovery,thousands of highly conserved miRNAs have been identified and investigated for their role in human health and diseases.MiR-214 has been increasingly reported to have an association with the regulation of bone metabolism.Reports suggested that miR-214 controls the critical aspects of osteoblasts(bone-forming cells),including their differentiation,proliferation,viability,and migration.Studies have also reported the functional significance of miR-214 in bone diseases and suggested its candidature as a diagnostic and therapeutic target.Further,targeting miR-214 by other ncRNAs,such as linear ncRNAs and circular RNAs,has provided novel insights into treating bone diseases.This review briefly discusses the contemporary findings of the physiological and pathological roles of miR-214 in bone turnover.In addition,we highlight the important ncRNA/mRNA/miR-214 axes influencing osteoblast differentiation that are of therapeutic importance for the treatment of bone-related diseases.展开更多
Photovoltaic(PV)boards are a perfect way to create eco-friendly power from daylight.The defects in the PV panels are caused by various conditions;such defective PV panels need continuous monitoring.The recent developm...Photovoltaic(PV)boards are a perfect way to create eco-friendly power from daylight.The defects in the PV panels are caused by various conditions;such defective PV panels need continuous monitoring.The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants.In general,conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation.The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process.To increase the accuracy and to reduce the processing time,a new Convolutional Neural Network(CNN)architecture is required.Hence,in the present work,a new Real-time Multi Variant Deep learning Model(RMVDM)architecture is proposed,and it extracts the image features and classifies the defects in PV panels quickly with high accuracy.The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images.The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person.The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset.The results show that 98%of the accuracy and recall values in the fault detection and classification process.展开更多
The introduction of new technologies has increased communication network coverage and the number of associating nodes in dynamic communication networks(DCN).As the network has the characteristics like decentralized an...The introduction of new technologies has increased communication network coverage and the number of associating nodes in dynamic communication networks(DCN).As the network has the characteristics like decentralized and dynamic,few nodes in the network may not associate with other nodes.These uncooperative nodes also known as selfish nodes corrupt the performance of the cooperative nodes.Namely,the nodes cause congestion,high delay,security concerns,and resource depletion.This study presents an effective selfish node detection method to address these problems.The Price of Anarchy(PoA)and the Price of Stability(PoS)in Game Theory with the Presence of Nash Equilibrium(NE)are discussed for the Selfish Node Detection.This is a novel experiment to detect selfish nodes in a network using PoA.Moreover,the least response dynamic-based Capacitated Selfish Resource Allocation(CSRA)game is introduced to improve resource usage among the nodes.The suggested strategy is simulated using the Solar Winds simulator,and the simulation results show that,when compared to earlier methods,the new scheme offers promising performance in terms of delivery rate,delay,and throughput.展开更多
The adsorption and diffusion of F2 molecules on pristine graphene are studied by using first-principles calculations.For the diffusion of F2 from molecular state in gas phase to the dissociative adsorption state on gr...The adsorption and diffusion of F2 molecules on pristine graphene are studied by using first-principles calculations.For the diffusion of F2 from molecular state in gas phase to the dissociative adsorption state on graphene surface, a kinetic barrier is identified, which explains the inertness of graphene in molecular F2 at room temperature, and its reactivity with F2 at higher temperatures. Study of the diffusion of F2 molecules on graphene surface determines the energy barrier along the optimal diffusion pathway, which conduces to the understanding of the high stability of fluorographene.展开更多
Traffic signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and...Traffic signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and indivi-duals by walking,etc.As a segment of the clever transportation structure,the acknowledgment of traffic signs is basic for the driving assistance system,traffic sign upkeep,self-administering driving,and various spaces.There are different assessments turns out achieved for traffic sign acknowledgment in the world.However,most of the works are only for explicit arrangements of traffic signs,for example,beyond what many would consider a possible sign.Traffic sign recognizable proof is generally seen as trying on account of various complexities,for example,extended establishments of traffic sign pictures.Two critical issues exist during the time spent identification(ID)and affirmation of traffic signals.Road signs are occasionally blocked not entirely by various vehicles and various articles are accessible in busy time gridlock scenes which make the signed acknowledgment hard and walkers,various vehicles,constructions,and loads up may frustrate the ID structure by plans like that of road signs.Also concealing information from traffic scene pictures is affected by moving light achieved by environment conditions,time(day-night),and shadowing.Traffic sign revelation and affirmation structure has two guideline sorts out:The essential stage incorpo-rates the traffic sign limitation and the resulting stage portrays the perceived traffic signs into a particular class.展开更多
Cancer therapy is a fast-emerging biomedical paradigm that elevates the diagnostic and therapeutic potential of a nanovector for identification,monitoring,targeting,and post-treatment response analysis.Nanovectors of ...Cancer therapy is a fast-emerging biomedical paradigm that elevates the diagnostic and therapeutic potential of a nanovector for identification,monitoring,targeting,and post-treatment response analysis.Nanovectors of superparamagnetic iron oxide nanoparticles(SPION)are of tremendous significance in cancer therapy because of their inherited high surface area,high reactivity,biocompatibility,superior contrast,and magnetic and photo-inducibility properties.In addition to a brief introduction,we summarize various progressive aspects of nanomagnets pertaining to their production with an emphasis on sustainable biomimetic approaches.Post-synthesis particulate and surface alterations in terms of pharmaco-affinity,liquid accessibility,and biocompatibility to facilitate cancer therapy are highlighted.SPION parameters including particle contrast,core-fusions,surface area,reactivity,photosensitivity,photodynamics,and photothermal properties,which facilitate diverse cancer diagnostics,are discussed.We also elaborate on the concept of magnetism to selectively focus chemotherapeutics on tumors,cell sorting,purification of bioentities,and elimination of toxins.Finally,while addressing the toxicity of nanomaterials,the advent of ultrasmall nanomagnets as a healthier alternative with superior properties and compatible cellular interactions is reviewed.In summary,these discussions spotlight the versatility and integration of multitasking nanomagnets and ultrasmall nanomagnets for diverse cancer theragnostics.展开更多
In recent days the usage of android smartphones has increased exten-sively by end-users.There are several applications in different categories bank-ing/finance,social engineering,education,sports andfitness,and many mor...In recent days the usage of android smartphones has increased exten-sively by end-users.There are several applications in different categories bank-ing/finance,social engineering,education,sports andfitness,and many more applications.The android stack is more vulnerable compared to other mobile plat-forms like IOS,Windows,or Blackberry because of the open-source platform.In the Existing system,malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware.The attackers target the victim with various attacks like adware,backdoor,spyware,ransomware,and zero-day exploits and create threat hunts on the day-to-day basics.In the existing approach,there are various tradi-tional machine learning classifiers for building a decision support system with limitations such as low detection rate and less feature selection.The important contents taken for building model from android applications like Intent Filter,Per-mission Signature,API Calls,and System commands are taken from the manifestfile.The function parameters of various machine and deep learning classifiers like Nave Bayes,k-Nearest Neighbors(k-NN),Support Vector Machine(SVM),Ada Boost,and Multi-Layer Perceptron(MLP)are done for effective results.In our pro-posed work,we have used an unsupervised learning multilayer perceptron with multiple target labels and built a model with a better accuracy rate compared to logistic regression,and rank the best features for detection of applications and clas-sify as malicious or benign can be used as threat model by online antivirus scanners.展开更多
In this paper,Modified Multi-scale Segmentation Network(MMU-SNet)method is proposed for Tamil text recognition.Handwritten texts from digi-tal writing pad notes are used for text recognition.Handwritten words recognit...In this paper,Modified Multi-scale Segmentation Network(MMU-SNet)method is proposed for Tamil text recognition.Handwritten texts from digi-tal writing pad notes are used for text recognition.Handwritten words recognition for texts written from digital writing pad through text file conversion are challen-ging due to stylus pressure,writing on glass frictionless surfaces,and being less skilled in short writing,alphabet size,style,carved symbols,and orientation angle variations.Stylus pressure on the pad changes the words in the Tamil language alphabet because the Tamil alphabets have a smaller number of lines,angles,curves,and bends.The small change in dots,curves,and bends in the Tamil alphabet leads to error in recognition and changes the meaning of the words because of wrong alphabet conversion.However,handwritten English word recognition and conversion of text files from a digital writing pad are performed through various algorithms such as Support Vector Machine(SVM),Kohonen Neural Network(KNN),and Convolutional Neural Network(CNN)for offline and online alphabet recognition.The proposed algorithms are compared with above algorithms for Tamil word recognition.The proposed MMU-SNet method has achieved good accuracy in predicting text,about 96.8%compared to other traditional CNN algorithms.展开更多
Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique techniq...Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm.This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow movements.Then,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter.Then,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt Algorithm.The results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG signals.This proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes place.Maximum accuracy of 97.2%and maximum error range of 0.6684%are achieved during the analysis.Results of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of literature.Based on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying the results.These results would aid the specialists in their decision-making by facilitating the ana-lysis and interpretation of brain signals in the field of neuroscience,specifically in tremor analysis in PD.展开更多
Introduction： Oral health is window to overall health. There is a greatest burden of oral diseases on the underprivileged group. In developing countries like India the affordability to oral health care services is ve...Introduction： Oral health is window to overall health. There is a greatest burden of oral diseases on the underprivileged group. In developing countries like India the affordability to oral health care services is very limited thereby leading to poor oral wellness ＆ millions suffer intractable toothache and poor quality of living and end up with few dentition. Objective： To assess the utilization level of oral health services among women in Chennai. Material and methods： A cross-sectional questionnaire survey was conducted among 200 women in Teynampet Zone in Chennai District, Tamil Nadu. The women were chosen by simple random sampling and were interviewed using the semi-stzuctured questionnaire to assess their utilization level during the period of June to July 2016. The data were analyzed by SPSS Version 22. Result： Descriptive statistics and multivariate analysis--MANOVA were used to analyze the utilization level. Majority of the respondents were in the age group of 30-35years, most of the respondents had oral problem and almost everyone had visited dentist at least once within 3 years. Multivariate analysis--MANOVA also showed that the utilization levels were directly influenced by accessibility, availability and affordability and showed statistical significance （p value 〈 0.05） and also from MANOVA analysis it showed that the respondents who had poor oral hygiene did not utilize oral health care services as the affordability was a problem although accessibility and availability was adequate. Conclusion： Our fmdings suggest that people who had oral problem had visited dentist in previous 3 years and most of the people who visited dentist had a good oral hygiene. Cost of the treatment affected the dental visits. They believed that visiting the dentist is necessary only for pain relief.展开更多
文摘The novelty of this research lies in the successful fabrication of a 3D-printed honeycomb structure filled with nanofillers for acoustic properties,utilizing an impedance tube setup in accordance with ASTM standard E 1050-12.The Creality Ender-3,a 3D printer,was used for printing the honeycomb structures,and polylactic acid(PLA)material was employed for their construction.The organic,inorganic,and polymeric compounds within the composites were identified using fourier transformation infrared(FTIR)spectroscopy.The structure and homogeneity of the samples were examined using a field emission scanning electron microscope(FESEM).To determine the sound absorption coefficient of the 3D printed honeycomb structure,numerous samples were systematically developed using central composite design(CCD)and analysed using response surface methodology(RSM).The RSM mathematical model was established to predict the optimum values of each factor and noise reduction coefficient(NRC).The optimum values for an NRC of 0.377 were found to be 1.116 wt% carbon black,1.025 wt% aluminium powder,and 3.151 mm distance between parallel edges.Overall,the results demonstrate that a 3Dprinted honeycomb structure filled with nanofillers is an excellent material that can be utilized in various fields,including defence and aviation,where lightweight and acoustic properties are of great importance.
文摘Whole grains of proso and barnyard millets were sequentially extracted using different solvents(hexane,chloroform,ethyl acetate,and methanol).Phytochemical analysis was performed qualitatively,and the total phenolic content in the extracts of proso and barnyard millets was quantified.Alkaloids and cardiac glycosides were identified in all solvent extracts of both millets.Anthraquinone and glycosides yielded negative results in all solvent extracts of both millets.Among all the solvent extracts,methanol extracts of proso and barnyard millets showed the presence of major compounds such as flavonoids,terpenoids,amino acids,tannins,and phenolics compounds.The maximum amount of phenols was found in methanolic extracts of proso and barnyard millets(0.669±0.003 and 0.625±0.003),followed by the chloroform extract of proso and barnyard millets(0.284±0.002 and 0.257±0.003).The minimum amount of phenolics was found in the acetone extract of proso and barnyard millets.The methanol extract of both millets showed the presence of major compounds with high phenolic content.
基金the Research Grant of Kwangwoon University in 2024.
文摘Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.
文摘Fuel cells have attracted extensive attention due to their high conversion efficiency and environmental friendliness.However,their wider application is limited by the poor activity and high cost of platinum(Pt),which is widely used as the cathode catalyst to overcome the slow kinetics associated with oxygen reduction reaction(ORR).Pt‐based composites with one‐dimensional(1D)nanoarchitectures demonstrate great advantages towards efficient ORR catalysis.This review focuses on the recent advancements in the design and synthesis of 1D Pt‐based ORR catalysts.After introducing the fundamental ORR mechanism and the advanced 1D architectures,their synthesis strategies(template‐based and template‐free methods)are discoursed.Subsequently,their morphology and structure optimization are highlighted,followed by the superstructure assembly using 1D Pt‐based blocks.Finally,the challenges and perspectives on the synthesis innovation,structure design,physical characterization,and theoretical investigations are proposed for 1D Pt‐based ORR nanocatalysts.We anticipate this study will inspire more research endeavors on efficient ORR nanocatalysts in fuel cell application.
基金The present work was financially supported by a Grant-Aid for Science and Engineering Research Board(Grant No.SERB/F/922/2014-15),Department of Science&Technology,Government of India.
文摘We show the results of first-principles calculations of structural,phonon,elastic,thermal and electronic properties of the Mg-X inter-metallics in their respective ground state phase and meta-stable phases at equilibrium geometry and the studied pressure range.Phonon dispersion spectra for these compounds were investigated by using the linear response technique.The phonon spectra do not show any abnormality in their respective ground state phase.The respective ground states phases of the studied system remain stable within the studied pressure range.Electronic and thermodynamic properties were derived by analysis of the electronic structures and quasi-harmonic approximation.The mixed bonding character of the Mg-X intermetallics is revealed by Mg-X bonds,and it leads the metallic nature.Most of the contribution originated from X ions d like states at Fermi level compared to that of Mg ion in these intermetallics.In this work,we also predicted the melting temperature of these intermetallics and evaluated the Debye temperature by using elastic constants.
基金the financial support from the National Natural Science Foundations of China (21374008)the Beijing Forbidden City Scholarship (2018420021)。
文摘Bridging the performance gap of the electrocatalyst between the rotating disk electrode(RDE) and membrane electrode assembly(MEA) level testing is the key to reducing the total cost of proton exchange membrane fuel cell(PEMFC) vehicles. Presently, platinum metal accounts for ~42% of the total cost of the PEMFC vehicles for usage in the cathode catalyst layer, where the sluggish oxygen reduction reaction(ORR) occurs. An alternative to the platinum catalyst, the Fe-N-C catalyst has attracted considerable interest for PEMFC due to its cost-effectiveness and high catalytic activity towards ORR. However, the excellent ORR activity of Fe-N-C obtained from RDE studies rarely translates the same performance into MEA operating conditions. Such a performance gap is mainly attributed to the lack of atomic-level understanding of Fe-N-C active sites and their ORR mechanism. Besides, unless the cost of expensive electrocatalyst is reduced, the total operation cost of the PEMFC vehicles remains constant. Therefore,developing highly efficient Fe-N-C catalysts from academic and industrial perspectives is critical for commercializing PEMFC vehicles. Here, the scope of the review is three-fold. First, we discussed the atomiclevel insights of Fe-N-C active sites and ORR mechanism, followed by unraveling the different iron-based nanostructured ORR electrocatalysts, including oxide, carbide, nitride, phosphide, sulfide, and singleatom catalysts. And then we bridged their ORR catalytic performance gap between the RDE and MEA tests for real operating conditions of PEMFC vehicles. Second, we focused on bridging the cost barriers of PEMFC vehicles between capital, operation, and end-user. Finally, we provided the path to achieve sustainable development goals by commercializing PEMFC vehicles for a better world.
文摘Android devices are popularly available in the commercial market at different price levels for various levels of customers.The Android stack is more vulnerable compared to other platforms because of its open-source nature.There are many android malware detection techniques available to exploit the source code andfind associated components during execution time.To obtain a better result we create a hybrid technique merging static and dynamic processes.In this paper,in thefirst part,we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Mul-ticollinearity problem is one of the drawbacks in the existing system.In the proposed work,a novel PCA(Principal Component Analysis)based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given approach.The Android Sensitive Permission is one major key point to be considered while detecting malware.We select vulnerable columns based on features like sensitive permissions,application program interface calls,services requested through the kernel,and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or benign.Thefinal goal of this paper is to check benchmarking datasets collected from various repositories like virus share,Github,and the Canadian Institute of cyber security,compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.
文摘Purpose:The aim of this study was to review,systematically,evidence concerning the link between the ACTN3 R577X polymorphism and the rates and severity of non-contact injuries and exercise-induced muscle damage in athletes and individuals enrolled in exercise training programs.Methods:A computerized literature search was performed in the electronic databases PubMed,Web of Science,and SPORTDiscus,from inception until November 2020.All included studies compared the epidemiological characteristics of non-contact injury between the different genotypes of the ACTN3 R577X polymorphism.Results:Our search identified 492 records.After the screening of titles,abstracts,and full texts,13 studies examining the association between the ACTN3 genotypes and the rate and severity of non-contact injury were included in the analysis.These studies were performed in 6 different countries(Spain,Japan,Brazil,China,the Republic of Korea,and Italy)and involved a total participant pool of 1093 participants.Of the studies,2 studies involved only women,5 studies involved only men,and 6 studies involved both men and women.All the studies included were classified as highquality studies(≥6 points in the Physiotherapy Evidence Database(PEDro)scale score).Overall,evidence suggests there is an association between the ACTN3 R577X genotype and non-contact injury in 12 investigations.Six studies observed a significant association between A CTN3 R577X polymorphism and exercise induced muscle damage:2 with non-contact ankle injury,3 with non-contact muscle injury,and 1 with overall non-contact injury.Conclusion:The present findings support the premise that possessing the ACTN3 XX genotype may predispose athletes to a higher probability of some non-contact injuries,such as muscle injury,ankle sprains,and higher levels of exercise-induced muscle damage.
基金Indian Council of Medical Research,2020-0282/SCR/ADHOC-BMSDepartment of Science and Technology,India,DST/INSPIRE Fellowship:2021/IF210073.
文摘Mesenchymal stem cells(MSCs)originate from many sources,including the bone marrow and adipose tissue,and differentiate into various cell types,such as osteoblasts and adipocytes.Recent studies on MSCs have revealed that many transcription factors and signaling pathways control osteogenic development.Osteogenesis is the process by which new bones are formed;it also aids in bone remodeling.Wnt/β-catenin and bone morphogenetic protein(BMP)signaling pathways are involved in many cellular processes and considered to be essential for life.Wnt/β-catenin and BMPs are important for bone formation in mammalian development and various regulatory activities in the body.Recent studies have indicated that these two signaling pathways contribute to osteogenic differen-tiation.Active Wnt signaling pathway promotes osteogenesis by activating the downstream targets of the BMP signaling pathway.Here,we briefly review the molecular processes underlying the crosstalk between these two pathways and explain their participation in osteogenic differentiation,emphasizing the canonical pathways.This review also discusses the crosstalk mechanisms of Wnt/BMP signaling with Notch-and extracellular-regulated kinases in osteogenic differentiation and bone development.
文摘The concept of inflammatory bowel disease(IBD),which encompasses Crohn’s disease and ulcerative colitis,represents a complex and growing global health concern resulting from a multifactorial etiology.Both dysfunctional autophagy and dysbiosis contribute to IBD,with their combined effects exacerbating the related inflammatory condition.As a result,the existing interconnection between gut microbiota,autophagy,and the host’s immune system is a decisive factor in the occurrence of IBD.The factors that influence the gut microbiota and their impact are another important point in this regard.Based on this initial perspective,this manuscript briefly highlighted the intricate interplay between the gut microbiota,autophagy,and IBD pathogenesis.In addition,it also addressed the potential targeting of the microbiota and modulating autophagic pathways for IBD therapy and proposed suggestions for future research within a more specific and expanded context.Further studies are warranted to explore restoring microbial balance and regulating autophagy mechanisms,which may offer new therapeutic avenues for IBD management and to delve into personalized treatment to alleviate the related burden.
基金supported by the Indian Council of Medical Research,India[File No.2020-0282/SCR/ADHOC-BMS to N.S.]Department of Science&Technology[DST/INSPIRE Fellowship/2019/IF190170 to R.L.A.,DST/INSPIRE Fellowship/2021/IF210073 to I.S.].
文摘MicroRNAs(miRNAs)are small non-coding RNAs(ncRNAs)that regulate the expression of their targetmRNAs post-transcriptionally.Since their discovery,thousands of highly conserved miRNAs have been identified and investigated for their role in human health and diseases.MiR-214 has been increasingly reported to have an association with the regulation of bone metabolism.Reports suggested that miR-214 controls the critical aspects of osteoblasts(bone-forming cells),including their differentiation,proliferation,viability,and migration.Studies have also reported the functional significance of miR-214 in bone diseases and suggested its candidature as a diagnostic and therapeutic target.Further,targeting miR-214 by other ncRNAs,such as linear ncRNAs and circular RNAs,has provided novel insights into treating bone diseases.This review briefly discusses the contemporary findings of the physiological and pathological roles of miR-214 in bone turnover.In addition,we highlight the important ncRNA/mRNA/miR-214 axes influencing osteoblast differentiation that are of therapeutic importance for the treatment of bone-related diseases.
文摘Photovoltaic(PV)boards are a perfect way to create eco-friendly power from daylight.The defects in the PV panels are caused by various conditions;such defective PV panels need continuous monitoring.The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants.In general,conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation.The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process.To increase the accuracy and to reduce the processing time,a new Convolutional Neural Network(CNN)architecture is required.Hence,in the present work,a new Real-time Multi Variant Deep learning Model(RMVDM)architecture is proposed,and it extracts the image features and classifies the defects in PV panels quickly with high accuracy.The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images.The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person.The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset.The results show that 98%of the accuracy and recall values in the fault detection and classification process.
文摘The introduction of new technologies has increased communication network coverage and the number of associating nodes in dynamic communication networks(DCN).As the network has the characteristics like decentralized and dynamic,few nodes in the network may not associate with other nodes.These uncooperative nodes also known as selfish nodes corrupt the performance of the cooperative nodes.Namely,the nodes cause congestion,high delay,security concerns,and resource depletion.This study presents an effective selfish node detection method to address these problems.The Price of Anarchy(PoA)and the Price of Stability(PoS)in Game Theory with the Presence of Nash Equilibrium(NE)are discussed for the Selfish Node Detection.This is a novel experiment to detect selfish nodes in a network using PoA.Moreover,the least response dynamic-based Capacitated Selfish Resource Allocation(CSRA)game is introduced to improve resource usage among the nodes.The suggested strategy is simulated using the Solar Winds simulator,and the simulation results show that,when compared to earlier methods,the new scheme offers promising performance in terms of delivery rate,delay,and throughput.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11664003 and 11474285)the Natural Science Foundation of Guangxi Zhuang Autonomous Region,China(Grant No.2015GXNSFAA139015)the Scientific Research and Technology Development Program of Guilin,China(Grant No.2016012002)
文摘The adsorption and diffusion of F2 molecules on pristine graphene are studied by using first-principles calculations.For the diffusion of F2 from molecular state in gas phase to the dissociative adsorption state on graphene surface, a kinetic barrier is identified, which explains the inertness of graphene in molecular F2 at room temperature, and its reactivity with F2 at higher temperatures. Study of the diffusion of F2 molecules on graphene surface determines the energy barrier along the optimal diffusion pathway, which conduces to the understanding of the high stability of fluorographene.
文摘Traffic signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and indivi-duals by walking,etc.As a segment of the clever transportation structure,the acknowledgment of traffic signs is basic for the driving assistance system,traffic sign upkeep,self-administering driving,and various spaces.There are different assessments turns out achieved for traffic sign acknowledgment in the world.However,most of the works are only for explicit arrangements of traffic signs,for example,beyond what many would consider a possible sign.Traffic sign recognizable proof is generally seen as trying on account of various complexities,for example,extended establishments of traffic sign pictures.Two critical issues exist during the time spent identification(ID)and affirmation of traffic signals.Road signs are occasionally blocked not entirely by various vehicles and various articles are accessible in busy time gridlock scenes which make the signed acknowledgment hard and walkers,various vehicles,constructions,and loads up may frustrate the ID structure by plans like that of road signs.Also concealing information from traffic scene pictures is affected by moving light achieved by environment conditions,time(day-night),and shadowing.Traffic sign revelation and affirmation structure has two guideline sorts out:The essential stage incorpo-rates the traffic sign limitation and the resulting stage portrays the perceived traffic signs into a particular class.
基金Department of Science and Technology,Government of India,NewDelhi,for financial support through Early Career Research Award(Grant No.:ECR/2017/000339).
文摘Cancer therapy is a fast-emerging biomedical paradigm that elevates the diagnostic and therapeutic potential of a nanovector for identification,monitoring,targeting,and post-treatment response analysis.Nanovectors of superparamagnetic iron oxide nanoparticles(SPION)are of tremendous significance in cancer therapy because of their inherited high surface area,high reactivity,biocompatibility,superior contrast,and magnetic and photo-inducibility properties.In addition to a brief introduction,we summarize various progressive aspects of nanomagnets pertaining to their production with an emphasis on sustainable biomimetic approaches.Post-synthesis particulate and surface alterations in terms of pharmaco-affinity,liquid accessibility,and biocompatibility to facilitate cancer therapy are highlighted.SPION parameters including particle contrast,core-fusions,surface area,reactivity,photosensitivity,photodynamics,and photothermal properties,which facilitate diverse cancer diagnostics,are discussed.We also elaborate on the concept of magnetism to selectively focus chemotherapeutics on tumors,cell sorting,purification of bioentities,and elimination of toxins.Finally,while addressing the toxicity of nanomaterials,the advent of ultrasmall nanomagnets as a healthier alternative with superior properties and compatible cellular interactions is reviewed.In summary,these discussions spotlight the versatility and integration of multitasking nanomagnets and ultrasmall nanomagnets for diverse cancer theragnostics.
文摘In recent days the usage of android smartphones has increased exten-sively by end-users.There are several applications in different categories bank-ing/finance,social engineering,education,sports andfitness,and many more applications.The android stack is more vulnerable compared to other mobile plat-forms like IOS,Windows,or Blackberry because of the open-source platform.In the Existing system,malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware.The attackers target the victim with various attacks like adware,backdoor,spyware,ransomware,and zero-day exploits and create threat hunts on the day-to-day basics.In the existing approach,there are various tradi-tional machine learning classifiers for building a decision support system with limitations such as low detection rate and less feature selection.The important contents taken for building model from android applications like Intent Filter,Per-mission Signature,API Calls,and System commands are taken from the manifestfile.The function parameters of various machine and deep learning classifiers like Nave Bayes,k-Nearest Neighbors(k-NN),Support Vector Machine(SVM),Ada Boost,and Multi-Layer Perceptron(MLP)are done for effective results.In our pro-posed work,we have used an unsupervised learning multilayer perceptron with multiple target labels and built a model with a better accuracy rate compared to logistic regression,and rank the best features for detection of applications and clas-sify as malicious or benign can be used as threat model by online antivirus scanners.
文摘In this paper,Modified Multi-scale Segmentation Network(MMU-SNet)method is proposed for Tamil text recognition.Handwritten texts from digi-tal writing pad notes are used for text recognition.Handwritten words recognition for texts written from digital writing pad through text file conversion are challen-ging due to stylus pressure,writing on glass frictionless surfaces,and being less skilled in short writing,alphabet size,style,carved symbols,and orientation angle variations.Stylus pressure on the pad changes the words in the Tamil language alphabet because the Tamil alphabets have a smaller number of lines,angles,curves,and bends.The small change in dots,curves,and bends in the Tamil alphabet leads to error in recognition and changes the meaning of the words because of wrong alphabet conversion.However,handwritten English word recognition and conversion of text files from a digital writing pad are performed through various algorithms such as Support Vector Machine(SVM),Kohonen Neural Network(KNN),and Convolutional Neural Network(CNN)for offline and online alphabet recognition.The proposed algorithms are compared with above algorithms for Tamil word recognition.The proposed MMU-SNet method has achieved good accuracy in predicting text,about 96.8%compared to other traditional CNN algorithms.
文摘Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm.This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow movements.Then,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter.Then,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt Algorithm.The results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG signals.This proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes place.Maximum accuracy of 97.2%and maximum error range of 0.6684%are achieved during the analysis.Results of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of literature.Based on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying the results.These results would aid the specialists in their decision-making by facilitating the ana-lysis and interpretation of brain signals in the field of neuroscience,specifically in tremor analysis in PD.
文摘Introduction： Oral health is window to overall health. There is a greatest burden of oral diseases on the underprivileged group. In developing countries like India the affordability to oral health care services is very limited thereby leading to poor oral wellness ＆ millions suffer intractable toothache and poor quality of living and end up with few dentition. Objective： To assess the utilization level of oral health services among women in Chennai. Material and methods： A cross-sectional questionnaire survey was conducted among 200 women in Teynampet Zone in Chennai District, Tamil Nadu. The women were chosen by simple random sampling and were interviewed using the semi-stzuctured questionnaire to assess their utilization level during the period of June to July 2016. The data were analyzed by SPSS Version 22. Result： Descriptive statistics and multivariate analysis--MANOVA were used to analyze the utilization level. Majority of the respondents were in the age group of 30-35years, most of the respondents had oral problem and almost everyone had visited dentist at least once within 3 years. Multivariate analysis--MANOVA also showed that the utilization levels were directly influenced by accessibility, availability and affordability and showed statistical significance （p value 〈 0.05） and also from MANOVA analysis it showed that the respondents who had poor oral hygiene did not utilize oral health care services as the affordability was a problem although accessibility and availability was adequate. Conclusion： Our fmdings suggest that people who had oral problem had visited dentist in previous 3 years and most of the people who visited dentist had a good oral hygiene. Cost of the treatment affected the dental visits. They believed that visiting the dentist is necessary only for pain relief.
