How to"pick up"colorectal serrated lesions and polyps in daily histopathology practice:From terminologies to diagnostic pitfalls

Over the last decade,our knowledge of colorectal serrated polyps and lesions has significantly improved due to numerous studies on this group of precursor lesions.Serrated lesions were misleading as benign before 2010,but they are currently reclassified as precancerous lesions that contribute to 30%of colorectal cancer through the serrated neoplasia pathway.The World Health Organization updated the classification for serrated lesions and polyps of the colon and rectum in 2019,which is more concise and applicable in daily practice.The responsible authors prescribe that“colorectal serrated lesions and polyps are characterized by a serrated(sawtooth or stellate)architecture of the epithelium.”From a clinical standpoint,sessile serrated lesion(SSL)and SSL with dysplasia(SSLD)are the two most significant entities.Despite these advancements,the precise diagnosis of SSL and SSLD based mainly on histopathology remains challenging due to various difficulties.This review describes the nomenclature and the terminology of colorectal serrated polyps and lesions and highlights the diagnostic criteria and obstacles encountered in the histopathological diagnosis of SSL and SSLD.

Exploring Phonetic Differences and Cross-Linguistic Influences: A Comparative Study of English and Mandarin Chinese Pronunciation Patterns

Exploring the differences in phonemes and cross-linguistic influences, particularly by comparing the pronunciation patterns of English and Mandarin, is crucial for language learners. Such comparative studies can help learners better understand and overcome the pronunciation difficulties encountered during second language acquisition. English and Mandarin have significant differences in their vowel and consonant systems, tones, intonation, and syllable structures. A deep understanding of these differences allows learners to engage in targeted pronunciation training, reducing accent interference. This research provides guidance for improving language teaching methods, enabling teachers to design more effective pronunciation strategies and exercises based on the students’ native language backgrounds, thus enhancing learning outcomes. Additionally, cross-linguistic studies aid in improving speech recognition and conversion technologies, achieving higher accuracy and naturalness in multilingual speech processing systems. From a cultural exchange perspective, understanding and respecting the phonetic characteristics of different languages help to enhance the effectiveness and mutual understanding in cross-cultural communication. The comparative study of English and Mandarin pronunciation patterns not only provides a theoretical foundation for language education and technological applications but also promotes effective communication in multilingual environments. This thesis uses a comparative research method to elucidate the study of English and Mandarin pronunciation patterns. It begins with an analysis of the characteristics and similarities of the pronunciation patterns in both languages. The thesis then examines the differences between English and Mandarin pronunciation patterns through four aspects: the number and complexity of vowel phonemes, types and distribution of consonant phonemes, tones and stress, and intonation and phonetic phenomena. Finally, based on the comparative analysis of the differences, the thesis offers targeted policy recommendations for learning English and Mandarin.

Hiding on jagged karst pinnacles:A new microendemic genus and species of a limestone-dwelling agamid lizard(Squamata:Agamidae:Draconinae)from Khammouan Province,Laos

We describe a unique new species and genus of agamid lizard from the karstic massifs of Khammouan Province,central Laos.Laodracon carsticola Gen.et sp.nov.is an elusive medium-sized lizard(maximum snout-vent length101 mm)specifically adapted to life on limestone rocks and pinnacles.To assess the phylogenetic position of the new genus amongst other agamids,we generated DNA sequences from two mitochondrial gene fragments(16S rRNA and ND2)and three nuclear loci(BDNF,RAG1 and c-mos),with a final alignment comprising 7418 base pairs for 64 agamid species.Phylogenetic analyses unambiguously place the new genus in the mainland Asia subfamily Draconinae,where it forms a clade sister to the genus Diploderma from East Asia and the northern part of Southeast Asia.Morphologically,the new genus is distinguished from all other genera in Draconinae by possessing a notably swollen tail base with enlarged scales on its dorsal and ventral surfaces.Our work provides further evidence that limestone regions of Indochina represent unique“arks of biodiversity”and harbor numerous relict lineages.To date,Laodracon carsticola Gen.et sp.nov.is known from only two adult male specimens and its distribution seems to be restricted to a narrow limestone massif on the border of Khammouan and Bolikhamxai provinces of Laos.Additional studies are required to understand its life history,distribution,and conservation status.

Mountain jade: A new high-elevation microendemic species of the genus Zhangixalus(Amphibia: Anura: Rhacophoridae)from Laos

DEAR EDITOR,We report on a new species, Zhangixalus melanoleucus sp.nov., from Phou Samsoum Mountain(PSM) in Xiengkhouang Province, northeastern Laos, based on an integrative taxonomic approach, including morphological, molecular, and bioacoustic lines of evidence. Morphologically, the new species can be distinguished from its congeners by a combination of the following diagnostic characters: medium body size(SVL 34.4–36.3 mm in males, 53.7 mm in a single female);dorsum smooth and green;chest and belly lacking spots;flanks, axillae, ventral surfaces of forearms, inguinal.

Recent advances and innovations in the design and fabrication of wearable flexible biosensors and human health monitoring systems based on conjugated polymers

Wearable biosensors have received great interest as patient-friendly diagnostic technologies because of their high flexibility and conformability.The growing research and utilization of novel materials in designing wearable biosensors have accelerated the development of point-of-care sensing platforms and implantable biomedical devices in human health care.Among numerous potential materials,conjugated polymers(CPs)are emerging as ideal choices for constructing high-performance wearable biosensors because of their outstanding conductive and mechanical properties.Recently,CPs have been extensively incorporated into various wearable biosensors to monitor a range of target biomolecules.However,fabricating highly reliable CP-based wearable biosensors for practical applications remains a significant challenge,necessitating novel developmental strategies for enhancing the viability of such biosensors.Accordingly,this review aims to provide consolidated scientific evidence by summarizing and evaluating recent studies focused on designing and fabricating CP-based wearable biosensors,thereby facilitating future research.Emphasizing the superior properties and benefits of CPs,this review aims to clarify their potential applicability within this field.Furthermore,the fundamentals and main components of CP-based wearable biosensors and their sensing mechanisms are discussed in detail.The recent advancements in CP nanostructures and hybridizations for improved sensing performance,along with recent innovations in next-generation wearable biosensors are highlighted.CPbased wearable biosensors have been—and will continue to be—an ideal platform for developing effective and user-friendly diagnostic technologies for human health monitoring.

Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

Blasting in surface mines aims to fragment rock masses to a proper size.However,flyrock is an undesirable effect of blasting that can result in human injuries.In this study,support vector regression(SVR)is combined with four algorithms:gravitational search algorithm(GSA),biogeography-based optimization(BBO),ant colony optimization(ACO),and whale optimization algorithm(WOA)for predicting flyrock in two surface mines in Iran.Additionally,three other methods,including artificial neural network(ANN),kernel extreme learning machine(KELM),and general regression neural network(GRNN),are employed,and their performances are compared to those of four hybrid SVR models.After modeling,the measured and predicted flyrock values are validated with some performance indices,such as root mean squared error(RMSE).The results revealed that the SVR-WOA model has the most optimal accuracy,with an RMSE of 7.218,while the RMSEs of the KELM,GRNN,SVR-GSA,ANN,SVR-BBO,and SVR-ACO models are 10.668,10.867,15.305,15.661,16.239,and 18.228,respectively.Therefore,combining WOA and SVR can be a valuable tool for accurately predicting flyrock distance in surface mines.

A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle ReducedAnisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm(GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.

Exploring the Interplay between Job Stress,Work Performance,and Attitudes toward Professional Psychological Help among Employees

Objective:This study explores the interplay between job stress,job-related factors,work performance,and attitudes toward seeking professional psychological help among Vietnamese employees.Methods:A total of 374 employees in Vietnam were surveyed using random sampling and an online questionnaire from November 07 to November 28,2023.Demographic data and self-reported from three scales:The New Job Stress Scale(NJSS),Work Performance(WP),and Attitudes Toward Seeking Professional Psychological Help(ATSPPH_SF)were collected.Results:Significant variations were found across several variables,including forms of work,operating hours,education levels,monthly income,numbers of daily working hours,and the presence of a psychological counseling department within the company.Our analysis has highlighted direct relationships between key latent variables.Employees who were more open to seeking professional help tended to report higher levels of job stress.The negative relationship was found between job stress and attitudes toward seeking professional help.Additionally,work effort was positively associated with work quality.Moderation analyses revealed the influence of co-worker support on role expectation conflict and work effort,role expectation conflict and work-life balance,as well as interactions between role expectation conflict and attitudes needed toward seeking professional help.Mediation analyses showed that work effort mediated relationships between openness to seeking professional help,co-worker support,work-life balance,role expectation conflict,and work quality.Attitudes toward seeking professional help also mediated relationships between work-life balance,job stress,and work quality.Conclusion:The study highlights the complex dynamics surrounding job stress,job-related factors,work performance,and attitudes toward seeking professional psychological help among Vietnamese employees.It highlights the importance of addressing help-seeking barriers,promoting work engagement,and fostering healthy work-life balance for employee well-being and productivity.Further research across diverse contexts and interventions is needed.

Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and privacy.Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT.In this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine(ACOMKSVM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data sharing.This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data,collected from various data providers.Then,ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process.In this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed ledger.The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts.To examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI repository.The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.

Memetic Optimization with Cryptographic Encryption for Secure Medical Data Transmission in IoT-Based Distributed Systems

In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medical tests.This datum is sensitive,and hence security is a must in transforming the sensational contents.In this paper,an Evolutionary Algorithm,namely the Memetic Algorithm is used for encrypting the text messages.The encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 levels.The reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the encoded letter.To show its precision,equivalent to five RGB images and five Grayscale images are used to test the proposed algorithm.The results of the proposed algorithm were analyzed using statistical methods,and the proposed algorithm showed the importance of data transfer in healthcare systems in a stable environment.In the future,to embed the privacy-preserving of medical data,it can be extended with blockchain technology.

Optimal treatment of stratified Carreau and Casson nanofluids flows in Darcy-Forchheimer porous space over porous matrix

A comparative three-dimensional(3D)analysis for Casson-nanofluid and Carreau-nanofluid flows due to a flat body in a magnetohydrodynamic(MHD)stratified environment is presented.Flow is estimated to be suspended in a Darcy-Forchheimer medium.Soret and Dufour responses are also accommodated in the flow field.A moving(rotating)coordinate system is exercised to examine the bidirectionally stretched flow fields(flow,heat transfer,and mass transfer).Nanofluid is compounded by taking ethylene glycol/sodium alginate as base fluid and ferric-oxide(Fe3O4)as nanoparticles.Governing equations are handled by the application of optimal homotopy asymptotic method(OHAM),where convergence parameters are optimized through the classical least square procedure.The novel mechanism(hidden physics)due to appearing parameters is explored with the assistance of tabular and graphical expositions.Outcomes reveal the double behavior state for temperature field with thermal stratification/Dufour number,and for concentration field with Soret number due to the presence of turning points.

Analysis of Unsteady Heat Transfer Problems with Complex Geometries Using Isogeometric Boundary Element Method

Numerical analysis of unsteady heat transfer problems with complex geometries by the isogeometric boundary element method(IGABEM)is presented.The IGABEM possesses many desirable merits and features,for instance,(a)exactly represented arbitrarily complex geometries,and higher-order continuity due to non-uniform rational B-splines(NURBS)shape functions;(b)using NURBS for both field approximation and geometric description;(c)directly utilizing geometry data from computer-aided design(CAD);and(d)only boundary discretization.The formulation of IGABEM for unsteady heat transfer is derived.The domain discretization in terms of IGABEM for unsteady heat transfer is required as that in traditional BEM.The internal values however are obtained with the analytical formula according to the values on the boundaries,and its computations are therefore mainly dependent on the discretization of the boundaries.The coordinates of internal control points are obtained with the coordinates of control points on the boundaries using Coons body interpolation method.The developed approach is tested with several numerical examples from simple to complicated geometries.Good agreement is gained with reference solutions derived from either analytical or finite element methods.

Dealing with Imbalanced Dataset Leveraging Boundary Samples Discovered by Support Vector Data Description

These days,imbalanced datasets,denoted throughout the paper by ID,(a dataset that contains some(usually two)classes where one contains considerably smaller number of samples than the other(s))emerge in many real world problems(like health care systems or disease diagnosis systems,anomaly detection,fraud detection,stream based malware detection systems,and so on)and these datasets cause some problems(like under-training of minority class(es)and over-training of majority class(es),bias towards majority class(es),and so on)in classification process and application.Therefore,these datasets take the focus of many researchers in any science and there are several solutions for dealing with this problem.The main aim of this study for dealing with IDs is to resample the borderline samples discovered by Support Vector Data Description(SVDD).There are naturally two kinds of resampling:Under-sampling(U-S)and oversampling(O-S).The O-S may cause the occurrence of over-fitting(the occurrence of over-fitting is its main drawback).The U-S can cause the occurrence of significant information loss(the occurrence of significant information loss is its main drawback).In this study,to avoid the drawbacks of the sampling techniques,we focus on the samples that may be misclassified.The data points that can be misclassified are considered to be the borderline data points which are on border(s)between the majority class(es)and minority class(es).First by SVDD,we find the borderline examples;then,the data resampling is applied over them.At the next step,the base classifier is trained on the newly created dataset.Finally,we compare the result of our method in terms of Area Under Curve(AUC)and F-measure and G-mean with the other state-of-the-art methods.We show that our method has betterresults than the other state-of-the-art methods on our experimental study.

Calibration of a neutron dose rate meter in various neutron standard fields

This paper presents the calibration of a neutron dose rate meter and the evaluation of its calibration factors(CFs)in several neutron standard fields(i.e.,two standard fields with bare sources of252Cf and241Am-Be,and five simulated workplace fields with241Am-Be moderated sources).The calibration in standard fields with bare sources was conducted by following the recommendations of the ISO 8529 standard.The measured total neutron ambient dose equivalent rates,denoted as H*(10)tot,were analyzed to obtain direct components,denoted as H*(10)dir,using a reduced fitting method.The CF was then calculated as the ratio between the conventional true value of the neutron ambient dose equivalent rate in a free field,denoted as H*(10)FF,and the value of H*(10)dir.In contrast,in the simulated workplace neutron fields,the calibration of the neutron dose rate meter was conducted by following the ISO 12789 standard.The CF was calculated as the ratio between the values of H*(10)totmeasured by a standard instrument(i.e.,Bonner sphere spectrometer)and the neutron dose rate meter.The CF values were obtained in the range of 0.88–1.0.The standard uncertainties(k=1)of the CFs were determined to be in the range of approximately 6.6–13.1%.

Generating A New Shilling Attack for Recommendation Systems

A collaborative filtering-based recommendation system has been an integral part of e-commerce and e-servicing.To keep the recommendation systems reliable,authentic,and superior,the security of these systems is very crucial.Though the existing shilling attack detection methods in collaborative filtering are able to detect the standard attacks,in this paper,we prove that they fail to detect a new or unknown attack.We develop a new attack model,named Obscure attack,with unknown features and observed that it has been successful in biasing the overall top-N list of the target users as intended.The Obscure attack is able to push target items to the top-N list as well as remove the actual rated items from the list.Our proposed attack is more effective at a smaller number of k in top-k similar user as compared to other existing attacks.The effectivity of the proposed attack model is tested on the MovieLens dataset,where various classifiers like SVM,J48,random forest,and naïve Bayes are utilized.

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

In the past few decades,climatic changes led by environmental pollution,the emittance of greenhouse gases,and the emergence of brown energy utilization have led to global warming.Global warming increases the Earth’s temperature,thereby causing severe effects on human and environmental conditions and threatening the livelihoods of millions of people.Global warming issues are the increase in global temperatures that lead to heat strokes and high-temperature-related diseases during the summer,causing the untimely death of thousands of people.To forecast weather conditions,researchers have utilized machine learning algorithms,such as autoregressive integrated moving average,ensemble learning,and long short-term memory network.These techniques have been widely used for the prediction of temperature.In this paper,we present a swarm-based approach called Cauchy particle swarm optimization(CPSO)to find the hyperparameters of the long shortterm memory(LSTM)network.The hyperparameters were determined by minimizing the LSTM validationmean square error rate.The optimized hyperparameters of the LSTM were used to forecast the temperature of Chennai City.The proposed CPSO-LSTM model was tested on the openly available 25-year Chennai temperature dataset.The experimental evaluation on MATLABR2020a analyzed the root mean square error rate and mean absolute error to evaluate the forecasted output.The proposed CPSO-LSTM outperforms the traditional LSTM algorithm by reducing its computational time to 25 min under 200 epochs and 150 hidden neurons during training.The proposed hyperparameter-based LSTM can predict the temperature accurately by having a root mean square error(RMSE)value of 0.250 compared with the traditional LSTM of 0.35 RMSE.

Modeling viscosity of methane,nitrogen,and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

Accurate gas viscosity determination is an important issue in the oil and gas industries.Experimental approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT).In this study,a number of correlations were developed to estimate gas viscosity by the use of group method of data handling(GMDH)type neural network and gene expression programming(GEP)techniques using a large data set containing more than 3000 experimental data points for methane,nitrogen,and hydrocarbon gas mixtures.It is worth mentioning that unlike many of viscosity correlations,the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 K.Also,a comparison was performed between the results of these established models and the results of ten wellknown models reported in the literature.Average absolute relative errors of GMDH models were obtained 4.23%,0.64%,and 0.61%for hydrocarbon gas mixtures,methane,and nitrogen,respectively.In addition,graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT conditions.Also,using leverage technique,valid,suspected and outlier data points were determined.Finally,trends of gas viscosity models at different conditions were evaluated.

Forced Vibration Analysis of Functionally Graded Anisotropic Nanoplates Resting on Winkler/Pasternak-Foundation

This study investigates the forced vibration of functionally graded hexagonal nano-size plates for the first time.A quasi-three-dimensional(3D)plate theory including stretching effect is used to model the anisotropic plate as a continuum one where small-scale effects are considered based on nonlocal strain gradient theory.Also,the plate is assumed on a Pasternak foundation in which normal and transverse shear loads are taken into account.The governing equations of motion are obtained via the Hamiltonian principles which are solved using analytical based methods by means of Navier’s approximation.The influences of the exponential factor,nonlocal parameter,strain gradient parameter,Pasternak foundation coefficients,length-to-thickness,and length-to-width ratios on the dynamic response of the nanoplates are examined.In addition,the accuracy of an isotropic approximate instead of the anisotropic model is studied.The dynamic behavior of the system shows that mechanical mathematics-based models may get better results considering the anisotropic model because the dynamic response can cause prominent differences(up to 17%)between isotropic approximation and anisotropic model.

One-step hydrothermal synthesis of Sn-doped α-Fe_(2)O_(3) nanoparticles for enhanced photocatalytic degradation of Congo red

We report on the synthesis of Sn-doped hematite nanoparticles(Sn-α-Fe_(2)O_(3) NPs)by the hydrothermal method.The prepared Sn-α-Fe_(2)O_(3) NPs had a highly pure and well crystalline rhombohedral phase with an average particle size of 41.4 nm.The optical properties of as-synthesizedα-Fe_(2)O_(3) NPs show a higher bandgap energy(2.40-2.57 eV)than that of pure bulkα-Fe_(2)O_(3)(2.1 eV).By doping Sn intoα-Fe_(2)O_(3) NPs,the Sn-doped hematite was observed a redshift toward a long wavelength with in-creasing Sn concentration from 0%to 4.0%.The photocatalytic activity of Sn-dopedα-Fe_(2)O_(3) NPs was evaluated by Congo red(CR)dye degradation.The degradation efficiency of CR dye using Sn-α-Fe_(2)O_(3) NPs catalyst is higher than that of pureα-Fe_(2)O_(3) NPs.The highest degradation efficiency of CR dye was 97.8%using 2.5%Sn-dopedα-Fe_(2)O_(3) NPs catalyst under visible-light irradi-ation.These results suggest that the synthesized Sn-dopedα-Fe_(2)O_(3) nanoparticles might be a suitable approach to develop a photocatalytic degradation of toxic inorganic dye in wastewater.

Favipiravir and its potentials in COVID-19 pandemic:An update

The COVID-19 pandemic has caused millions of deaths and hundreds of millions of confirmed infections worldwide.This pandemic has prompted researchers to produce medications or vaccines to reduce or stop the progression and spread of this disease.A variety of previously licensed and marketed medications are being tested for the treatment and recurrence of SARS-CoV2,including favipiravir(Avigan).Favipiravir was recognized as an influenza antiviral drug in Japan in 2014,and has been known to have a potential in vitro activity against SARS-CoV-2,in addition to its broad therapeutic safety scope.Favipiravir was recently approved and officially used in many countries worldwide.Our review provides insights and up-to-date knowledge of the current role of favipiravir in the treatment of COVID-19 infection,focusing on preclinical and ongoing clinical trials,evidence of its efficacy against SARS-CoV-2 in COVID-19,side effects,anti-viral mechanism,and the pharmacokinetic properties of the drug in the treatment of COVID-19.Due to its teratogenic effects,favipiravir cannot be offered to expectant or pregnant mothers.The practical efficacy of such an intervention regimen will depend on its dose,treatment duration,and cost as well as difficulties in application.