Solar-driven CO_(2) conversion to methane and methanol using different nanostructured Cu_(2)O-based catalysts modified with Au nanoparticles

This work describes the use of TiO_(2)nanotubes-based electrodes(TNT)modified with Cu_(2)O nanostructures and gold nanoparticles for the photoelectroreduction of CO_(2)to produce value-added compounds.A thin layer of polydopamine was used as both an adherent agent and an electron transfer mediator,due to itsπ-conjugated electron system.The highest production yield was achieved using a TNT@PDA/Nc/Au40%electrode,with Faradaic efficiencies of 47.4%(110.5μM cm^(-2))and 27.8%(50.4μM cm^(-2))for methanol and methane,respectively.The performance of the photoelectrodes was shown to be Cu_(2)O facet-dependent,with cubic structures leading to greater conversion of CO_(2)to methanol(43%)and methane(27%),compared to the octahedral morphology,while a higher percentage of metallic gold on the nanostructured Cu_(2)O surface was mainly important for CH4production.Density functional theory(DFT)calculations supported these findings,attributing the superior photoelectrocatalytic performance of the TNT@PDA/Nc/Au40%electrode for CH4generation to the formation of an OCH3intermediate bonded to Au atoms.Studies using isotope-labeling and analysis by gas chromatograph-mass(GC-MS)demonstrated that13CO_(2)was the source for photoelectrocatalytic generation of13CH3OH and13CH313CH2OH.

Archery Algorithm:A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

Finding a suitable solution to an optimization problem designed in science is a major challenge.Therefore,these must be addressed utilizing proper approaches.Based on a random search space,optimization algorithms can find acceptable solutions to problems.Archery Algorithm(AA)is a new stochastic approach for addressing optimization problems that is discussed in this study.The fundamental idea of developing the suggested AA is to imitate the archer’s shooting behavior toward the target panel.The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer.The AA is mathematically described,and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions.Furthermore,the proposed algorithm’s performance is compared vs.eight approaches,including teaching-learning based optimization,marine predators algorithm,genetic algorithm,grey wolf optimization,particle swarm optimization,whale optimization algorithm,gravitational search algorithm,and tunicate swarm algorithm.According to the simulation findings,the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios,and it can give adequate quasi-optimal solutions to these problems.The analysis and comparison of competing algorithms’performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.

MLA:A New Mutated Leader Algorithm for Solving Optimization Problems

Optimization plays an effective role in various disciplines of science and engineering.Optimization problems should either be optimized using the appropriate method(i.e.,minimization or maximization).Optimization algorithms are one of the efficient and effective methods in providing quasioptimal solutions for these type of problems.In this study,a new algorithm called the Mutated Leader Algorithm(MLA)is presented.The main idea in the proposed MLA is to update the members of the algorithm population in the search space based on the guidance of a mutated leader.In addition to information about the best member of the population,themutated leader also contains information about the worst member of the population,as well as other normal members of the population.The proposed MLA is mathematically modeled for implementation on optimization problems.A standard set consisting of twenty-three objective functions of different types of unimodal,fixed-dimensional multimodal,and high-dimensional multimodal is used to evaluate the ability of the proposed algorithm in optimization.Also,the results obtained from theMLA are compared with eight well-known algorithms.The results of optimization of objective functions show that the proposed MLA has a high ability to solve various optimization problems.Also,the analysis and comparison of the performance of the proposed MLA against the eight compared algorithms indicates the superiority of the proposed algorithm and ability to provide more suitable quasi-optimal solutions.

Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

Air pollution is one of the major concerns considering detriments to human health.This type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye infections.Air pollution also poses serious issues to the planet.Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions.Thus,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions.The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks.Localization is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not optimal.This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities.These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as PM2.5 particulate matter,PM10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization.The dataset is divided into training and testing parts based on 10 cross-validations.The evaluation on predicting the air pollutant for localization is performed with the training dataset.Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.

Migration Algorithm:A New Human-BasedMetaheuristic Approach for Solving Optimization Problems

This paper introduces a newmetaheuristic algorithmcalledMigration Algorithm(MA),which is helpful in solving optimization problems.The fundamental inspiration of MA is the process of human migration,which aims to improve job,educational,economic,and living conditions,and so on.Themathematicalmodeling of the proposed MAis presented in two phases to empower the proposed approach in exploration and exploitation during the search process.In the exploration phase,the algorithm population is updated based on the simulation of choosing the migration destination among the available options.In the exploitation phase,the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions.MA’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite.In addition,MA’s results are compared with the performance of twelve well-known metaheuristic algorithms.The optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems.The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions.Also,the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications.

Binding of naturally occurring hydroxycinnamic acids to bovine serum albumin

Hydroxycinnamic acids (HCAs) possess numer- ous biological effects including antioxidant, antiallergic, antimicrobial, and immunomodulatory activities and due to these properties are widely used in folk medicine. Nevertheless, they can interact with protein molecules and cause some structural and functional changes. The possib- ility of HCAs binding to bovine serum albumin (BSA) under physiological conditions was inve- stigated by the UV-VIS absorption spectroscopy and fluorescence quenching method. Apart from rosmarinic acid, all tested HCAs quenched tryptophan fluorescence of BSA in the studied range of concentrations (0-20 μM) mainly by static quenching mechanism (formation of non- fluorescent HCA-BSA complexes). The binding constants, number of binding sites and free energy changes were determined. The binding affinities of HCAs were ranked in the order: chlorogenic acid > sinapic acid ≥ caffeic acid > ferulic acid > o-coumaric acid > p-coumaric acid ≥ m-coumaric acid, which was confirmed by spectral overlaps of BSA emission spectrum with absorption spectrum of HCA. All free energy changes possessed negative sign indicating the spontaneity of HCA-BSA interaction.

AMBO:All Members-Based Optimizer for Solving Optimization Problems

There are many optimization problems in different branches of science that should be solved using an appropriate methodology.Populationbased optimization algorithms are one of the most efficient approaches to solve this type of problems.In this paper,a new optimization algorithm called All Members-Based Optimizer(AMBO)is introduced to solve various optimization problems.The main idea in designing the proposedAMBOalgorithm is to use more information from the population members of the algorithm instead of just a few specific members(such as best member and worst member)to update the population matrix.Therefore,in AMBO,any member of the population can play a role in updating the population matrix.The theory of AMBO is described and then mathematically modeled for implementation on optimization problems.The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions,which belong to three different categories:unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions.In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO,eight other well-known algorithms have been also implemented.The optimization results demonstrate the ability of AMBO to solve various optimization problems.Also,comparison and analysis of the results show that AMBO is superior andmore competitive than the other mentioned algorithms in providing suitable solution.

Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a laboratory.PSG typically provides accurate results,but it is expensive and time consuming.However,for people with Sleep apnea(SA),available beds and laboratories are limited.Resultantly,it may produce inaccurate diagnosis.Thus,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)classifier.This paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen level.This diagnosis information is securely stored in the IoMT fog computing network.Doctors can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing network.The proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.

Language Education Optimization: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

In this paper,based on the concept of the NFL theorem,that there is no unique algorithm that has the best performance for all optimization problems,a new human-based metaheuristic algorithm called Language Education Optimization(LEO)is introduced,which is used to solve optimization problems.LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules.LEO is mathematically modeled in three phases:(i)students selecting their teacher,(ii)students learning from each other,and(iii)individual practice,considering exploration in local search and exploitation in local search.The performance of LEO in optimization tasks has been challenged against fifty-two benchmark functions of a variety of unimodal,multimodal types and the CEC 2017 test suite.The optimization results show that LEO,with its acceptable ability in exploration,exploitation,and maintaining a balance between them,has efficient performance in optimization applications and solution presentation.LEO efficiency in optimization tasks is compared with ten well-known metaheuristic algorithms.Analyses of the simulation results show that LEO has effective performance in dealing with optimization tasks and is significantly superior andmore competitive in combating the compared algorithms.The implementation results of the proposed approach to four engineering design problems show the effectiveness of LEO in solving real-world optimization applications.

SSABA:Search Step Adjustment Based Algorithm

Finding the suitable solution to optimization problems is a fundamental challenge in various sciences.Optimization algorithms are one of the effective stochastic methods in solving optimization problems.In this paper,a new stochastic optimization algorithm called Search StepAdjustment Based Algorithm(SSABA)is presented to provide quasi-optimal solutions to various optimization problems.In the initial iterations of the algorithm,the step index is set to the highest value for a comprehensive search of the search space.Then,with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal,the step index is reduced to reach the minimum value at the end of the algorithm implementation.SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types.The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm.In addition,the performance of the proposed SSABA is compared with the performance of eight well-known algorithms,including Particle Swarm Optimization(PSO),Genetic Algorithm(GA),Teaching-Learning Based Optimization(TLBO),Gravitational Search Algorithm(GSA),Grey Wolf Optimization(GWO),Whale Optimization Algorithm(WOA),Marine Predators Algorithm(MPA),and Tunicate Swarm Algorithm(TSA).The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.

Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information.Based on the characteristics of these intruders,many researchers attempted to aim to detect the intrusion with the help of automating process.Since,the large volume of data is generated and transferred through network,the security and performance are remained an issue.IDS(Intrusion Detection System)was developed to detect and prevent the intruders and secure the network systems.The performance and loss are still an issue because of the features space grows while detecting the intruders.In this paper,deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and preprocessing.The proposed system includes three phases such as preprocessing,feature selection and classification.In the first phase,KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization method.In second phase,feature selection is performed by using Information Gain based Dragonfly Optimizer(IGDFO).Finally,Deep clustering based Convolutional Neural Network(CCNN)classifier optimized with Particle Swarm Optimization(PSO)identifies intrusion attacks efficiently.The clustering loss and network loss can be reduced with the optimization algorithm.We evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation metrics.The experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy,precision,recall,f-measure and false detection rate.

A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics

There is a growing interest in the study development of artificial intelligence and machine learning,especially regarding the support vector machine pattern classification method.This study proposes an enhanced implementation of the well-known whale optimisation algorithm,which combines chaotic and opposition-based learning strategies,which is adopted for hyper-parameter optimisation and feature selection machine learning challenges.The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation.The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and support vector machine hyper-parameter tuning and validated for medical diagnostics by using breast cancer,diabetes,and erythemato-squamous dataset.The performance of the proposed model is compared with multiple competitive support vector machine models boosted with other metaheuristics,including another improved whale optimisation approach,particle swarm optimisation algorithm,bacterial foraging optimisation algorithms,and genetic algorithms.Results of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.

COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification

Computerized tomography(CT)scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia.On the basis of the image analysis results of chest CT and X-rays,the severity of lung infection is monitored using a tool.Many researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and inefficient.To overcome these issues,our proposed study implements four cascaded stages.First,for pre-processing,a mean filter is used.Second,texture feature extraction uses principal component analysis(PCA).Third,a modified whale optimization algorithm is used(MWOA)for a feature selection algorithm.The severity of lung infection is detected on the basis of age group.Fourth,image classification is done by using the proposed MWOAwith the salp swarm algorithm(MWOA-SSA).MWOA-SSA has an accuracy of 97%,whereas PCA and MWOA have accuracies of 81%and 86%.The sensitivity rate of the MWOA-SSA algorithm is better that of than PCA(84.4%)and MWOA(95.2%).MWOA-SSA outperforms other algorithms with a specificity of 97.8%.This proposed method improves the effective classification of lung affected images from large datasets.

Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

At present,the prevalence of diabetes is increasing because the human body cannot metabolize the glucose level.Accurate prediction of diabetes patients is an important research area.Many researchers have proposed techniques to predict this disease through data mining and machine learning methods.In prediction,feature selection is a key concept in preprocessing.Thus,the features that are relevant to the disease are used for prediction.This condition improves the prediction accuracy.Selecting the right features in the whole feature set is a complicated process,and many researchers are concentrating on it to produce a predictive model with high accuracy.In this work,a wrapper-based feature selection method called recursive feature elimination is combined with ridge regression(L2)to form a hybrid L2 regulated feature selection algorithm for overcoming the overfitting problem of data set.Overfitting is a major problem in feature selection,where the new data are unfit to the model because the training data are small.Ridge regression is mainly used to overcome the overfitting problem.The features are selected by using the proposed feature selection method,and random forest classifier is used to classify the data on the basis of the selected features.This work uses the Pima Indians Diabetes data set,and the evaluated results are compared with the existing algorithms to prove the accuracy of the proposed algorithm.The accuracy of the proposed algorithm in predicting diabetes is 100%,and its area under the curve is 97%.The proposed algorithm outperforms existing algorithms.

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies.Early diagnosis of many diseases will improve the patient life.The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things(IoT),Wireless Sensor Networks(WSN),Embedded systems,Deep learning approaches and Optimization and aggregation methods.The data generated through these technologies will demand the bandwidth,data rate,latency of the network.In this proposed work,efficient discrete grey wolf optimization(DGWO)based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code(ECEMAC)has been used to aggregate the parameters generated from the wearable sensor devices of the patient.The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO.Aggregation scheme will reduce the number of transmissions over the network.The aggregated data are preprocessed at edge node to remove the noise for better diagnosis.Edge node will reduce the overhead of cloud server.The aggregated data are forward to cloud server for central storage and diagnosis.This proposed smart diagnosis will reduce the transmission cost through aggrega-tion scheme which will reduce the energy of the system.Energy cost for proposed system for 300 nodes is 0.34μJ.Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme(SPPDA),concealed data aggregation scheme for multiple application(CDAMA)and secure aggregation scheme(ASAS)are 1.3μJ,0.81μJ and 0.51μJ respectively.The optimization approaches and encryption method will ensure the data privacy.

Toward a sustainable growth path in Arab economies:an extension of classical growth model

Background/Objectives:Many economies are on the trajectory of alternative growth drivers other than conventional capital and labor.Access to credit facilities is a pertinent indicator of economic growth.In line with the United Nations Sustainable Development Goals(UNSDGs-8)agenda,the national goal for sustainable development for most economies and Arab economies is no exception.Therefore,the current study adopts a traditional growth model by exploring the relationship between gross domestic product(GDP)per capita,credit for private sectors,ratio of exports,real GDP,and per labor force participants for selected Arab economies annually from 2001 to 2020.Research design:This study leverages the Fourier Kwiatkowski–Phillips–Schmidt–Shin(KPSS)unit root test and second-generation panel econometrics as estimation techniques,such as Westerlund and Edgerton panel cointegration test,and the use of two estimators,namely the augmented mean group(AMG)and common correlated error mean group(CCEMG),to obtain robust results.Findings:Empirical findings from Westerlund and Edgerton panel cointegration tests validate the long-run equilibrium relationship among the outlined variables.Further empirical results indicate that the share of exports is negatively significant with economic growth in countries such as Kuwait,Lebanon,Tunisia,and Jordan.Additionally,savings and labor force participation have a positive relationship with economic growth in individual countries such as Algeria and Bahrain.As per the panel,there is no significant relationship between labor force participation and economic growth.This indicates that the skilled labor force enhanced economic growth.Conclusions:These findings come with inherent far-reaching policy suggestions for economies and panels.Further details on country-specific policy actions are presented in the concluding section.

Proof of Activity Protocol for IoMT Data Security

The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time interval.In,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical appearance.With the use of sensors,IoMT applications are used in healthcare management.In such applications,one of the most important factors is data security,given that its transmission over the network may cause obtrusion.For data security in IoMT systems,blockchain is used due to its numerous blocks for secure data storage.In this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare system.The main aim is to enhance the data security over the networks.The PoA protocol enhances high security of data from the literature review.By replacing the malicious node from the block,the PoA can provide high security for medical data in the blockchain.Comparison with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems.

Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions.The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.

Bulwark of Liberty or Backward Savagery?Dispute Between Rousseau and the Polish Enlightenment Thinkers Over Eastern Europe

This study aims to question the traditional interpretation of the Enlightenment discourse,which rests upon the assumption that Eastern Europeans were considered as uneducated savages(an image created by Western European elites)that need to be developed according to the principles of Western civilization.Such a view might be deemed incomplete and thus misleading.Of course,there have always been many“Western progressive thinkers”who promoted the idea of westernization of the East,as well as there have been plenty of Eastern Enlightenment intellectuals holding onto Western ideals as a means of salvation from backwardness.However,one should admit that the original Eastern structures of state and society represented an inspiring alternative that enabled some theoreticians to get a different viewpoint;as obvious in the Polish case.Taking the uniqueness of their 18th century political system into account,one is able to acknowledge the importance of the Polish internal debate that tried to answer whether a republican spirit of the state was something that should have been preserved or completely rejected.In terms of the East-Western dichotomical point of view however,it is essential that this contradiction between republic as“bulwark of freedom”and“backward barbarism”did not only represent a local issue,but also piqued Western curiosity,especially Rousseau’s one.Hence,it is fully legitimate to analyse his approach towards this Eastern European country and his conclusions that contradicted both the Western as well as the Polish common convictions that the region was something undesirable.Thus,the main purpose of this paper is to analyse and explain the tensions between Rousseau and Polish Enlightenment thinkers such as M.Wielhorski or S.Leszczyński and,by clarifying them,to reveal a deeper ambiguity of the Enlightenment discourse concerning the interpretation of Eastern Europe as well as human nature.