Severe acute respiratory syndrome coronavirus(SARS-CoV)and SARS-CoV-2 are thought to transmit to humans via wild mammals,especially bats.However,evidence for direct bat-to-human transmission is lacking.Involvement of ...Severe acute respiratory syndrome coronavirus(SARS-CoV)and SARS-CoV-2 are thought to transmit to humans via wild mammals,especially bats.However,evidence for direct bat-to-human transmission is lacking.Involvement of intermediate hosts is considered a reason for SARS-CoV-2 transmission to humans and emergence of outbreak.Large biodiversity is found in tropical territories,such as Brazil.On the similar line,this study aimed to predict potential coronavirus hosts among Brazilian wild mammals based on angiotensin-converting enzyme 2(ACE2)sequences using evolutionary bioinformatics.Cougar,maned wolf,and bush dogs were predicted as potential hosts for coronavirus.These indigenous carnivores are philogenetically closer to the known SARS-CoV/SARS-CoV-2 hosts and presented low ACE2 divergence.A new coronavirus transmission chain was developed in which white-tailed deer,a susceptible SARS-CoV-2 host,have the central position.Cougar play an important role because of its low divergent ACE2 level in deer and humans.The discovery of these potential coronavirus hosts will be useful for epidemiological surveillance and discovery of interventions that can contribute to break the transmission chain.展开更多
There are quintillions of data on deoxyribonucleic acid(DNA)and protein in publicly accessible data banks,and that number is expanding at an exponential rate.Many scientific fields,such as bioinformatics and drug disc...There are quintillions of data on deoxyribonucleic acid(DNA)and protein in publicly accessible data banks,and that number is expanding at an exponential rate.Many scientific fields,such as bioinformatics and drug discovery,rely on such data;nevertheless,gathering and extracting data from these resources is a tough undertaking.This data should go through several processes,including mining,data processing,analysis,and classification.This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention.This software simulates the extraction of data from web-based(point-and-click)resources or graphical user interfaces that cannot be accessed using command-line tools.The software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides(AMP)sequences(46240 hits)from various MARVIN software panels,which can be later utilized to develop novel AMPs.Furthermore,for machine learning research,the program was validated by extracting 10,000 protein tertiary structures from the Protein Data Bank.As a result,data collection from the web will become faster and less expensive,with no need for manual data extraction.The software is critical as a first step to preparing large datasets for subsequent stages of analysis,such as those using machine and deep-learning applications.展开更多
Creative tourism is a dynamic and innovative approach to tourism,which points out the importance of people's active participation and their immersion in such experiences.In a vernacular context,it should attract p...Creative tourism is a dynamic and innovative approach to tourism,which points out the importance of people's active participation and their immersion in such experiences.In a vernacular context,it should attract people(local and tourists)attention to accomplish its main goals.Despite its rich cultural and natural assets,Kurdistan province faces several challenges that impact its tourism potential.To achieve that,the study uses quantitative approach to thoroughly analyze and evaluate the components of creative tourism in this province.The research focuses on tourists who visited the province's ten towns during the spring and summer of 2023.Data collection utilized a Likert-scale questionnaire ranging from"very good"to"very poor".The study employed a semistructured questionnaire developed through qualitative interviews alongside a researcher-made questionnaire validated by experts from the University of Kurdistan.The qualitative questionnaire achieved a high-reliability score of 93%using Cronbach's alpha coefficient.In-depth interviews and literary research were conducted to identify creative tourism components and indicators,informing the development of a quantitative questionnaire.Data analysis was performed using SPSS 20 and AMOS software to scrutinize the survey findings,providing insights into enhancing creative tourism strategies in Kurdistan province.The results reveal the varying significance of these dimensions,with the cultural dimension identified as the most crucial(factor loading:0.95),followed by the social(0.92),economic(0.88),and managerial/political dimensions(0.83).The study highlights the importance of cultural planning,community engagement,and infrastructural support in fostering creative tourism.Furthermore,it explores the impact of creative industries,such as music and arts rooted in Kurdish culture,on tourism development.Economic diversification and spatialphysical considerations are critical factors in enhancing Kurdistan's appeal as a creative tourism destination,emphasizing sustainable growth and cultural preservation.展开更多
The rapid increase in the number of Internet of Things(IoT)devices,coupled with a rise in sophisticated cyberattacks,demands robust intrusion detection systems.This study presents a holistic,intelligent intrusion dete...The rapid increase in the number of Internet of Things(IoT)devices,coupled with a rise in sophisticated cyberattacks,demands robust intrusion detection systems.This study presents a holistic,intelligent intrusion detection system.It uses a combined method that integrates machine learning(ML)and deep learning(DL)techniques to improve the protection of contemporary information technology(IT)systems.Unlike traditional signature-based or singlemodel methods,this system integrates the strengths of ensemble learning for binary classification and deep learning for multi-class classification.This combination provides a more nuanced and adaptable defense.The research utilizes the NF-UQ-NIDS-v2 dataset,a recent,comprehensive benchmark for evaluating network intrusion detection systems(NIDS).Our methodological framework employs advanced artificial intelligence techniques.Specifically,we use ensemble learning algorithms(Random Forest,Gradient Boosting,AdaBoost,and XGBoost)for binary classification.Deep learning architectures are also employed to address the complexities of multi-class classification,allowing for fine-grained identification of intrusion types.To mitigate class imbalance,a common problem in multi-class intrusion detection that biases model performance,we use oversampling and data augmentation.These techniques ensure equitable class representation.The results demonstrate the efficacy of the proposed hybrid ML-DL system.It achieves significant improvements in intrusion detection accuracy and reliability.This research contributes substantively to cybersecurity by providing a more robust and adaptable intrusion detection solution.展开更多
Long-period waves pose a threat to coastal communities as they propagate from deep ocean to shallow coastal waters. At the coastline, such waves have a greater height and longer period in comparison with local storm w...Long-period waves pose a threat to coastal communities as they propagate from deep ocean to shallow coastal waters. At the coastline, such waves have a greater height and longer period in comparison with local storm waves, and can cause severe inundation and damage. In this study,we considered linear long waves in a two-dimensional(vertical-horizontal) domain propagating towards a shoreline over a shallowing shelf.New solutions to the linear shallow water equations were found, through the separation of variables, for two forms of transition shelf morphology: deep water and shallow coastal water horizontal shelves connected by linear and parabolic transition, respectively. Expressions for the transmission and reflection coefficients are presented for each case. The analytical solutions were used to test the results from a novel computational scheme, which was then applied to extending the existing results relating to the reflected and transmitted components of an incident wave. The solutions and computational package provide new tools for coastal managers to formulate improved defence and riskmitigation strategies.展开更多
BACKGROUND: To identify the effects of sedative agent selection on morbidity, mortality, and length of stay in patients with suspected increase in intracranial pressure. Recent trends and developments have resulted in...BACKGROUND: To identify the effects of sedative agent selection on morbidity, mortality, and length of stay in patients with suspected increase in intracranial pressure. Recent trends and developments have resulted in changes to medications that were previously utilized as pharmacological adjuncts in the sedation and intubation of patients with suspected increases in intracranial pressure. Medications that were previously considered contraindicated are now being used with increasing regularity without demonstrated safety and effectiveness. The primary objective of this study is to evaluate and compare the use of Ketamine as an induction agent for patients with increased intracranial pressure. The secondary objective was to evaluate and compare the use of Etomidate, Midazolam, and Ketamine in patients with increased intracranial pressure. METHODS: We conducted a retrospective chart review of patients transported to our facility with evidence of intracranial hypertension that were intubated before trauma center arrival. Patients were identifi ed during a 22-month period from January 2014 to October 2015. Goals were to evaluate the impact of sedative agent selection on morbidity, mortality, and length of stay.RESULTS: During the review 148 patients were identifi ed as meeting inclusion criteria, 52 were excluded due to incomplete data. Of those the patients primarily received; Etomidate, Ketamine, and Midazolam. Patients in the Ketamine group were found to have a lower mortality rate after injury stratifi cation. CONCLUSION: Patients with intracranial hypertension should not be excluded from receiving Ketamine during intubation out of concern for worsening outcomes.展开更多
Background:Genotyping by sequencing(GBS)is a robust method to genotype markers.Many factors can influence the genotyping quality.One is that heterozygous genotypes could be wrongly genotyped as homozygotes,dependent o...Background:Genotyping by sequencing(GBS)is a robust method to genotype markers.Many factors can influence the genotyping quality.One is that heterozygous genotypes could be wrongly genotyped as homozygotes,dependent on the genotyping depths.In this study,a method correcting this type of genotyping error was demonstrated.The efficiency of this correction method and its effect on genomic prediction were assessed using simulated data of livestock populations.Results:Chip array(Chip)and four depths of GBS data was simulated.After quality control(call rate≥0.8 and MAF≥0.01),the remaining number of Chip and GBS SNPs were both approximately 7,000,averaged over 10 replicates.GBS genotypes were corrected with the proposed method.The reliability of genomic prediction was calculated using GBS,corrected GBS(GBSc),true genotypes for the GBS loci(GBSr)and Chip data.The results showed that GBSc had higher rates of correct genotype calls and higher correlations with true genotypes than GBS.For genomic prediction,using Chip data resulted in the highest reliability.As the depth increased to 10,the prediction reliabilities using GBS and GBSc data approached those using true GBS data.The reliabilities of genomic prediction using GBSc data were 0.604,0.672,0.684 and 0.704 after genomic correction,with the improved values of 0.013,0.009,0.006 and 0.001 at depth=2,4,5 and 10,respectively.Conclusions:The current study showed that a correction method for GBS data increased the genotype accuracies and,consequently,improved genomic predictions.These results suggest that a correction of GBS genotype is necessary,especially for the GBS data with low depths.展开更多
A wireless sensor network(WSN)consists of several tiny sensor nodes to monitor,collect,and transmit the physical information from an environment through the wireless channel.The node failure is considered as one of th...A wireless sensor network(WSN)consists of several tiny sensor nodes to monitor,collect,and transmit the physical information from an environment through the wireless channel.The node failure is considered as one of the main issues in the WSN which creates higher packet drop,delay,and energy consumption during the communication.Although the node failure occurred mostly due to persistent energy exhaustion during transmission of data packets.In this paper,Artificial Neural Network(ANN)based Node Failure Detection(NFD)is developed with cognitive radio for detecting the location of the node failure.The ad hoc on-demand distance vector(AODV)routing protocol is used for transmitting the data from the source node to the base station.Moreover,the Mahalanobis distance is used for detecting an adjacent node to the node failure which is used to create the routing path without any node failure.The performance of the proposed ANN-NFD method is analysed in terms of throughput,delivery rate,number of nodes alive,drop rate,end to end delay,energy consumption,and overhead ratio.Furthermore,the performance of the ANN-NFD method is evaluated with the header to base station and base station to header(H2B2H)protocol.The packet delivery rate of the ANN-NFD method is 0.92 for 150 nodes that are high when compared to the H2B2H protocol.Hence,the ANN-NFD method provides data consistency during data transmission under node and battery failure.展开更多
Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than ever.In IDS research,the most effectively used methodology is based on supervise...Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than ever.In IDS research,the most effectively used methodology is based on supervised Neural Networks(NN)and unsupervised clustering,but there are few works dedicated to their hybridization with metaheuristic algorithms.As intrusion detection data usually contains several features,it is essential to select the best ones appropriately.Linear Discriminant Analysis(LDA)and t-statistic are considered as efficient conventional techniques to select the best features,but they have been little exploited in IDS design.Thus,the research proposed in this paper can be summarized as follows.a)The proposed approach aims to use hybridized unsupervised and hybridized supervised detection processes of all the attack categories in the CICIDS2017 Dataset.Nevertheless,owing to the large size of the CICIDS2017 Dataset,only 25%of the data was used.b)As a feature selection method,the LDAperformancemeasure is chosen and combinedwith the t-statistic.c)For intrusion detection,unsupervised Fuzzy C-means(FCM)clustering and supervised Back-propagation NN are adopted.d)In addition and in order to enhance the suggested classifiers,FCM and NN are hybridized with the seven most known metaheuristic algorithms,including Genetic Algorithm(GA),Particle Swarm Optimization(PSO),Differential Evolution(DE),Cultural Algorithm(CA),Harmony Search(HS),Ant-Lion Optimizer(ALO)and Black Hole(BH)Algorithm.Performance metrics extracted from confusion matrices,such as accuracy,precision,sensitivity and F1-score are exploited.The experimental result for the proposed intrusion detection,based on training and test CICIDS2017 datasets,indicated that PSO,GA and ALO-based NNs can achieve promising results.PSO-NN produces a tested accuracy,global sensitivity and F1-score of 99.97%,99.95%and 99.96%,respectively,outperforming performance concluded in several related works.Furthermore,the best-proposed approaches are valued in the most recent intrusion detection datasets:CSE-CICIDS2018 and LUFlow2020.The evaluation fallouts consolidate the previous results and confirm their correctness.展开更多
Non-ionic deep eutectic solvents(DESs)are non-ionic designer solvents with various applications in catalysis,extraction,carbon capture,and pharmaceuticals.However,discovering new DES candidates is challenging due to a...Non-ionic deep eutectic solvents(DESs)are non-ionic designer solvents with various applications in catalysis,extraction,carbon capture,and pharmaceuticals.However,discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation.The search for DES relies heavily on intuition or trial-and-error processes,leading to low success rates or missed opportunities.Recognizing that hydrogen bonds(HBs)play a central role in DES formation,we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning(ML)models to discover new DES systems.We first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics(MD)simulation trajectories.The analysis reveals that DES systems have two unique features compared to non-DES systems:The DESs have①more imbalance between the numbers of the two intra-component HBs and②more and stronger inter-component HBs.Based on these results,we develop 30 ML models using ten algorithms and three types of HB-based descriptors.The model performance is first benchmarked using the average and minimal receiver operating characteristic(ROC)-area under the curve(AUC)values.We also analyze the importance of individual features in the models,and the results are consistent with the simulation-based statistical analysis.Finally,we validate the models using the experimental data of 34 systems.The extra trees forest model outperforms the other models in the validation,with an ROC-AUC of 0.88.Our work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.展开更多
The hydrological models and simpli?ed methods of Saint-venant equations are used extensively in hydrological modeling, in particular for the simulation of the ?ood routing. These models require speci?c and extensive d...The hydrological models and simpli?ed methods of Saint-venant equations are used extensively in hydrological modeling, in particular for the simulation of the ?ood routing. These models require speci?c and extensive data that usually makes the study of ?ood propagation an arduous practice. We present in this work a new model, based on a transfer function, this function is a function of parametric probability density, having a physical meaning with respect to the propagation of a hydrological signal. The inversion of the model is carried out by an optimization technique called Genetic Algorithm. It consists of evolving a population of parameters based primarily on genetic recombination operators and natural selection to?nd the minimum of an objective function that measures the distance between observed and simulated data. The precision of the simulations of the proposed model is compared with the response of the Hayami model and the applicability of the model is tested on a real case, the N'Fis basin river, located in the High Atlas Occidental, which presents elements that appear favorable to the study of the propagation. The results obtained are very satisfactory and the simulation of the proposed model is very close to the response of the Hayami model.展开更多
This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy model...This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.展开更多
Dear Editor,Middle East respiratory syndrome coronavirus（MERS-CoV）affected 1,621 patients worldwide,with a 36%mortality rate by the end of 2015.The highest number of cases was recorded in Saudi Arabia,
Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for re...Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for relevant patients with DoCs assessment, including brain-computer interfaces(BCIs). Recent progress in BCIs' clinical applications may offer important breakthroughs in the diagnosis and therapy of patients with DoCs. Thus the clinical significance of BCI applications in the diagnosis of patients with DoCs is hard to overestimate. One of them may be brain-computer interfaces. The aim of this study is to evaluate possibility of non-invasive EEG-based brain-computer interfaces in diagnosis of patients with DOCs in post-acute and long-term care institutions.展开更多
In England and Wales,the Mental Health Act(MHA)1983 provides the legal framework for the detention of individuals suffering from a mental disorder if they are judged to present a risk of harm to self or others.The MHA...In England and Wales,the Mental Health Act(MHA)1983 provides the legal framework for the detention of individuals suffering from a mental disorder if they are judged to present a risk of harm to self or others.The MHA removes from certain psychiatric patients civil liberties otherwise inherent in our legal system.Through both statute and common law,it balances a patient’s right to autonomy with psychiatrists'duty of care by reference to the health and safety of the patient.It also balances the civil rights of individual patients against the right of society to protection1.展开更多
Notwithstanding the discovery of vaccines for Covid-19, the virus'srapid spread continues due to the limited availability of vaccines, especially inpoor and emerging countries. Therefore, the key issues in the pre...Notwithstanding the discovery of vaccines for Covid-19, the virus'srapid spread continues due to the limited availability of vaccines, especially inpoor and emerging countries. Therefore, the key issues in the presentCOVID-19 pandemic are the early identification of COVID-19, the cautiousseparation of infected cases at the lowest cost and curing the disease in the earlystages. For that reason, the methodology adopted for this study is imaging tools,particularly computed tomography, which have been critical in diagnosing andtreating the disease. A new method for detecting Covid-19 in X-rays and CTimages has been presented based on the Scatter Wavelet Transform and DenseDeep Neural Network. The Scatter Wavelet Transform has been employed as afeature extractor, while the Dense Deep Neural Network is utilized as a binaryclassifier. An extensive experiment was carried out to evaluate the accuracy ofthe proposed method over three datasets: IEEE 80200, Kaggle, andCovid-19 X-ray image data Sets. The dataset used in the experimental part consists of 14142. The numbers of training and testing images are 8290 and 2810,respectively. The analysis of the result refers that the proposed methods achievedhigh accuracy of 98%. The proposed model results show an excellent outcomecompared to other methods in the same domain, such as (DeTraC) CNN, whichachieved only 93.1%, CNN, which achieved 94%, and stacked Multi-ResolutionCovXNet, which achieved 97.4%. The accuracy of CapsNet reached 97.24%.展开更多
Complex proteins are needed for many biological activities.Folding amino acid chains reveals their properties and functions.They support healthy tissue structure,physiology,and homeostasis.Precision medicine and treat...Complex proteins are needed for many biological activities.Folding amino acid chains reveals their properties and functions.They support healthy tissue structure,physiology,and homeostasis.Precision medicine and treatments require quantitative protein identification and function.Despite technical advances and protein sequence data exploration,bioinformatics’“basic structure”problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved.Protein function inference from amino acid sequences is the main biological data challenge.This study analyzes whether raw sequencing can characterize biological facts.A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.A coding technique for each sequence in each family was devised using two representations to identify each amino acid precisely.A bispectral analysis converts encoded protein numerical sequences into images for better protein sequence and family discrimination.Training and validation employed 70%of the dataset,while 30%was used for testing.This paper examined the performance of multistage deep learning models for differentiating between sixteen protein families after encoding and representing each encoded sequence by a higher spectral representation image(Bispectrum).Cascading minimized false positive and negative cases in all phases.The initial stage focused on two classes(six groups and ten groups).The subsequent stages focused on the few classes almost accurately separated in the first stage and decreased the overlapping cases between families that appeared in single-stage deep learning classification.The single-stage technique had 64.2%+/-22.8%accuracy,63.3%+/-17.1%precision,and a 63.2%+/19.4%F1-score.The two-stage technique yielded 92.2%+/-4.9%accuracy,92.7%+/-7.0%precision,and a 92.3%+/-5.0%F1-score.This work provides balanced,reliable,and precise forecasts for all families in all measures.It ensured that the new model was resilient to family variances and provided high-scoring results.展开更多
Proteins are essential for many biological functions.For example,folding amino acid chains reveals their functionalities by maintaining tissue structure,physiology,and homeostasis.Note that quantifiable protein charac...Proteins are essential for many biological functions.For example,folding amino acid chains reveals their functionalities by maintaining tissue structure,physiology,and homeostasis.Note that quantifiable protein characteristics are vital for improving therapies and precision medicine.The automatic inference of a protein’s properties from its amino acid sequence is called“basic structure”.Nevertheless,it remains a critical unsolved challenge in bioinformatics,although with recent technological advances and the investigation of protein sequence data.Inferring protein function from amino acid sequences is crucial in biology.This study considers using raw sequencing to explain biological facts using a large corpus of protein sequences and the Globin-like superfamily to generate a vector representation.The power of two representations was used to identify each amino acid,and a coding technique was established for each sequence family.Subsequently,the encoded protein numerical sequences are transformed into an image using bispectral analysis to identify essential characteristics for discriminating between protein sequences and their families.A deep Convolutional Neural Network(CNN)classifies the resulting images and developed non-normalized and normalized encoding techniques.Initially,the dataset was split 70/30 for training and testing.Correspondingly,the dataset was utilized for 70%training,15%validation,and 15%testing.The suggested methods are evaluated using accuracy,precision,and recall.The non-normalized method had 70%accuracy,72%precision,and 71%recall.68%accuracy,67%precision,and 67%recall after validation.Meanwhile,the normalized approach without validation had 92.4%accuracy,94.3%precision,and 91.1%recall.Validation showed 90%accuracy,91.2%precision,and 89.7%recall.Note that both algorithms outperform the rest.The paper presents that bispectrum-based nonlinear analysis using deep learning models outperforms standard machine learning methods and other deep learning methods based on convolutional architecture.They offered the best inference performance as the proposed approach improves categorization and prediction.Several instances show successful multi-class prediction in molecular biology’s massive data.展开更多
Denial of Service(DoS/DDoS)intrusions are damaging cyberattacks,and their identification is of great interest to the Intrusion Detection System(IDS).Existing IDS are mainly based on Machine Learning(ML)methods includi...Denial of Service(DoS/DDoS)intrusions are damaging cyberattacks,and their identification is of great interest to the Intrusion Detection System(IDS).Existing IDS are mainly based on Machine Learning(ML)methods including Deep Neural Networks(DNN),but which are rarely hybridized with other techniques.The intrusion data used are generally imbalanced and contain multiple features.Thus,the proposed approach aims to use a DNN-based method to detect DoS/DDoS attacks using CICIDS2017,CSE-CICIDS2018 and CICDDoS 2019 datasets,according to the following key points.a)Three imbalanced CICIDS2017-2018-2019 datasets,including Benign and DoS/DDoS attack classes,are used.b)A new technique based on K-means is developed to obtain semi-balanced datasets.c)As a feature selectionmethod,LDA(Linear Discriminant Analysis)performance measure is chosen.d)Four metaheuristic algorithms,counting Artificial Immune System(AIS),Firefly Algorithm(FA),Invasive Weeds Optimization(IWO)and Cuckoo Search(CS)are used,for the first time together,to increase the performance of the suggested DNN-based DoS attacks detection.The experimental results,based on semi-balanced training and test datasets,indicated that AIS,FA,IWO and CS-based DNNs can achieve promising results,even when cross-validated.AIS-DNN yields a tested accuracy of 99.97%,99.98%and 99.99%,for the three considered datasets,respectively,outperforming performance established in several related works.展开更多
文摘Severe acute respiratory syndrome coronavirus(SARS-CoV)and SARS-CoV-2 are thought to transmit to humans via wild mammals,especially bats.However,evidence for direct bat-to-human transmission is lacking.Involvement of intermediate hosts is considered a reason for SARS-CoV-2 transmission to humans and emergence of outbreak.Large biodiversity is found in tropical territories,such as Brazil.On the similar line,this study aimed to predict potential coronavirus hosts among Brazilian wild mammals based on angiotensin-converting enzyme 2(ACE2)sequences using evolutionary bioinformatics.Cougar,maned wolf,and bush dogs were predicted as potential hosts for coronavirus.These indigenous carnivores are philogenetically closer to the known SARS-CoV/SARS-CoV-2 hosts and presented low ACE2 divergence.A new coronavirus transmission chain was developed in which white-tailed deer,a susceptible SARS-CoV-2 host,have the central position.Cougar play an important role because of its low divergent ACE2 level in deer and humans.The discovery of these potential coronavirus hosts will be useful for epidemiological surveillance and discovery of interventions that can contribute to break the transmission chain.
基金This work was funded by the Graduate Scientific Research School at Yarmouk University under Grant Number:82/2020。
文摘There are quintillions of data on deoxyribonucleic acid(DNA)and protein in publicly accessible data banks,and that number is expanding at an exponential rate.Many scientific fields,such as bioinformatics and drug discovery,rely on such data;nevertheless,gathering and extracting data from these resources is a tough undertaking.This data should go through several processes,including mining,data processing,analysis,and classification.This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention.This software simulates the extraction of data from web-based(point-and-click)resources or graphical user interfaces that cannot be accessed using command-line tools.The software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides(AMP)sequences(46240 hits)from various MARVIN software panels,which can be later utilized to develop novel AMPs.Furthermore,for machine learning research,the program was validated by extracting 10,000 protein tertiary structures from the Protein Data Bank.As a result,data collection from the web will become faster and less expensive,with no need for manual data extraction.The software is critical as a first step to preparing large datasets for subsequent stages of analysis,such as those using machine and deep-learning applications.
基金supported by Exploring Visitor Satisfaction at Hunan's National Eco-Tourism Demonstration Zone in the Post-Pandemic Era:A Study(20YBA188)a project titled"Ecological and Limnological Study of the Zayibar International Wetland,Aiming to Provide Restoration Solutions"(S/01/9/10225),conducted at the Kurdistan Studies Research Institute,University of Kurdistan,in 2024。
文摘Creative tourism is a dynamic and innovative approach to tourism,which points out the importance of people's active participation and their immersion in such experiences.In a vernacular context,it should attract people(local and tourists)attention to accomplish its main goals.Despite its rich cultural and natural assets,Kurdistan province faces several challenges that impact its tourism potential.To achieve that,the study uses quantitative approach to thoroughly analyze and evaluate the components of creative tourism in this province.The research focuses on tourists who visited the province's ten towns during the spring and summer of 2023.Data collection utilized a Likert-scale questionnaire ranging from"very good"to"very poor".The study employed a semistructured questionnaire developed through qualitative interviews alongside a researcher-made questionnaire validated by experts from the University of Kurdistan.The qualitative questionnaire achieved a high-reliability score of 93%using Cronbach's alpha coefficient.In-depth interviews and literary research were conducted to identify creative tourism components and indicators,informing the development of a quantitative questionnaire.Data analysis was performed using SPSS 20 and AMOS software to scrutinize the survey findings,providing insights into enhancing creative tourism strategies in Kurdistan province.The results reveal the varying significance of these dimensions,with the cultural dimension identified as the most crucial(factor loading:0.95),followed by the social(0.92),economic(0.88),and managerial/political dimensions(0.83).The study highlights the importance of cultural planning,community engagement,and infrastructural support in fostering creative tourism.Furthermore,it explores the impact of creative industries,such as music and arts rooted in Kurdish culture,on tourism development.Economic diversification and spatialphysical considerations are critical factors in enhancing Kurdistan's appeal as a creative tourism destination,emphasizing sustainable growth and cultural preservation.
文摘The rapid increase in the number of Internet of Things(IoT)devices,coupled with a rise in sophisticated cyberattacks,demands robust intrusion detection systems.This study presents a holistic,intelligent intrusion detection system.It uses a combined method that integrates machine learning(ML)and deep learning(DL)techniques to improve the protection of contemporary information technology(IT)systems.Unlike traditional signature-based or singlemodel methods,this system integrates the strengths of ensemble learning for binary classification and deep learning for multi-class classification.This combination provides a more nuanced and adaptable defense.The research utilizes the NF-UQ-NIDS-v2 dataset,a recent,comprehensive benchmark for evaluating network intrusion detection systems(NIDS).Our methodological framework employs advanced artificial intelligence techniques.Specifically,we use ensemble learning algorithms(Random Forest,Gradient Boosting,AdaBoost,and XGBoost)for binary classification.Deep learning architectures are also employed to address the complexities of multi-class classification,allowing for fine-grained identification of intrusion types.To mitigate class imbalance,a common problem in multi-class intrusion detection that biases model performance,we use oversampling and data augmentation.These techniques ensure equitable class representation.The results demonstrate the efficacy of the proposed hybrid ML-DL system.It achieves significant improvements in intrusion detection accuracy and reliability.This research contributes substantively to cybersecurity by providing a more robust and adaptable intrusion detection solution.
基金supported by a Researcher Links Grant from the British Council,the Royal Academy of Engineering(Grant No.IAAP1/100086)the EFRaCC Project funded through the British Council's Global Innovation Initiative Program
文摘Long-period waves pose a threat to coastal communities as they propagate from deep ocean to shallow coastal waters. At the coastline, such waves have a greater height and longer period in comparison with local storm waves, and can cause severe inundation and damage. In this study,we considered linear long waves in a two-dimensional(vertical-horizontal) domain propagating towards a shoreline over a shallowing shelf.New solutions to the linear shallow water equations were found, through the separation of variables, for two forms of transition shelf morphology: deep water and shallow coastal water horizontal shelves connected by linear and parabolic transition, respectively. Expressions for the transmission and reflection coefficients are presented for each case. The analytical solutions were used to test the results from a novel computational scheme, which was then applied to extending the existing results relating to the reflected and transmitted components of an incident wave. The solutions and computational package provide new tools for coastal managers to formulate improved defence and riskmitigation strategies.
文摘BACKGROUND: To identify the effects of sedative agent selection on morbidity, mortality, and length of stay in patients with suspected increase in intracranial pressure. Recent trends and developments have resulted in changes to medications that were previously utilized as pharmacological adjuncts in the sedation and intubation of patients with suspected increases in intracranial pressure. Medications that were previously considered contraindicated are now being used with increasing regularity without demonstrated safety and effectiveness. The primary objective of this study is to evaluate and compare the use of Ketamine as an induction agent for patients with increased intracranial pressure. The secondary objective was to evaluate and compare the use of Etomidate, Midazolam, and Ketamine in patients with increased intracranial pressure. METHODS: We conducted a retrospective chart review of patients transported to our facility with evidence of intracranial hypertension that were intubated before trauma center arrival. Patients were identifi ed during a 22-month period from January 2014 to October 2015. Goals were to evaluate the impact of sedative agent selection on morbidity, mortality, and length of stay.RESULTS: During the review 148 patients were identifi ed as meeting inclusion criteria, 52 were excluded due to incomplete data. Of those the patients primarily received; Etomidate, Ketamine, and Midazolam. Patients in the Ketamine group were found to have a lower mortality rate after injury stratifi cation. CONCLUSION: Patients with intracranial hypertension should not be excluded from receiving Ketamine during intubation out of concern for worsening outcomes.
基金supported by the Genomic Selection in PlantsAnimals(GenSAP)research project financed by the Danish Council of Strategic Research(Aarhus,Denmark)the scholarship provided by the China Scholarship Council(CSC)
文摘Background:Genotyping by sequencing(GBS)is a robust method to genotype markers.Many factors can influence the genotyping quality.One is that heterozygous genotypes could be wrongly genotyped as homozygotes,dependent on the genotyping depths.In this study,a method correcting this type of genotyping error was demonstrated.The efficiency of this correction method and its effect on genomic prediction were assessed using simulated data of livestock populations.Results:Chip array(Chip)and four depths of GBS data was simulated.After quality control(call rate≥0.8 and MAF≥0.01),the remaining number of Chip and GBS SNPs were both approximately 7,000,averaged over 10 replicates.GBS genotypes were corrected with the proposed method.The reliability of genomic prediction was calculated using GBS,corrected GBS(GBSc),true genotypes for the GBS loci(GBSr)and Chip data.The results showed that GBSc had higher rates of correct genotype calls and higher correlations with true genotypes than GBS.For genomic prediction,using Chip data resulted in the highest reliability.As the depth increased to 10,the prediction reliabilities using GBS and GBSc data approached those using true GBS data.The reliabilities of genomic prediction using GBSc data were 0.604,0.672,0.684 and 0.704 after genomic correction,with the improved values of 0.013,0.009,0.006 and 0.001 at depth=2,4,5 and 10,respectively.Conclusions:The current study showed that a correction method for GBS data increased the genotype accuracies and,consequently,improved genomic predictions.These results suggest that a correction of GBS genotype is necessary,especially for the GBS data with low depths.
文摘A wireless sensor network(WSN)consists of several tiny sensor nodes to monitor,collect,and transmit the physical information from an environment through the wireless channel.The node failure is considered as one of the main issues in the WSN which creates higher packet drop,delay,and energy consumption during the communication.Although the node failure occurred mostly due to persistent energy exhaustion during transmission of data packets.In this paper,Artificial Neural Network(ANN)based Node Failure Detection(NFD)is developed with cognitive radio for detecting the location of the node failure.The ad hoc on-demand distance vector(AODV)routing protocol is used for transmitting the data from the source node to the base station.Moreover,the Mahalanobis distance is used for detecting an adjacent node to the node failure which is used to create the routing path without any node failure.The performance of the proposed ANN-NFD method is analysed in terms of throughput,delivery rate,number of nodes alive,drop rate,end to end delay,energy consumption,and overhead ratio.Furthermore,the performance of the ANN-NFD method is evaluated with the header to base station and base station to header(H2B2H)protocol.The packet delivery rate of the ANN-NFD method is 0.92 for 150 nodes that are high when compared to the H2B2H protocol.Hence,the ANN-NFD method provides data consistency during data transmission under node and battery failure.
文摘Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than ever.In IDS research,the most effectively used methodology is based on supervised Neural Networks(NN)and unsupervised clustering,but there are few works dedicated to their hybridization with metaheuristic algorithms.As intrusion detection data usually contains several features,it is essential to select the best ones appropriately.Linear Discriminant Analysis(LDA)and t-statistic are considered as efficient conventional techniques to select the best features,but they have been little exploited in IDS design.Thus,the research proposed in this paper can be summarized as follows.a)The proposed approach aims to use hybridized unsupervised and hybridized supervised detection processes of all the attack categories in the CICIDS2017 Dataset.Nevertheless,owing to the large size of the CICIDS2017 Dataset,only 25%of the data was used.b)As a feature selection method,the LDAperformancemeasure is chosen and combinedwith the t-statistic.c)For intrusion detection,unsupervised Fuzzy C-means(FCM)clustering and supervised Back-propagation NN are adopted.d)In addition and in order to enhance the suggested classifiers,FCM and NN are hybridized with the seven most known metaheuristic algorithms,including Genetic Algorithm(GA),Particle Swarm Optimization(PSO),Differential Evolution(DE),Cultural Algorithm(CA),Harmony Search(HS),Ant-Lion Optimizer(ALO)and Black Hole(BH)Algorithm.Performance metrics extracted from confusion matrices,such as accuracy,precision,sensitivity and F1-score are exploited.The experimental result for the proposed intrusion detection,based on training and test CICIDS2017 datasets,indicated that PSO,GA and ALO-based NNs can achieve promising results.PSO-NN produces a tested accuracy,global sensitivity and F1-score of 99.97%,99.95%and 99.96%,respectively,outperforming performance concluded in several related works.Furthermore,the best-proposed approaches are valued in the most recent intrusion detection datasets:CSE-CICIDS2018 and LUFlow2020.The evaluation fallouts consolidate the previous results and confirm their correctness.
基金supported by Ignite Research Collaborations(IRC),Startup funds,and the UK Artificial Intelligence(AI)in Medicine Research Alliance Pilot(NCATS UL1TR001998 and NCI P30 CA177558)。
文摘Non-ionic deep eutectic solvents(DESs)are non-ionic designer solvents with various applications in catalysis,extraction,carbon capture,and pharmaceuticals.However,discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation.The search for DES relies heavily on intuition or trial-and-error processes,leading to low success rates or missed opportunities.Recognizing that hydrogen bonds(HBs)play a central role in DES formation,we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning(ML)models to discover new DES systems.We first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics(MD)simulation trajectories.The analysis reveals that DES systems have two unique features compared to non-DES systems:The DESs have①more imbalance between the numbers of the two intra-component HBs and②more and stronger inter-component HBs.Based on these results,we develop 30 ML models using ten algorithms and three types of HB-based descriptors.The model performance is first benchmarked using the average and minimal receiver operating characteristic(ROC)-area under the curve(AUC)values.We also analyze the importance of individual features in the models,and the results are consistent with the simulation-based statistical analysis.Finally,we validate the models using the experimental data of 34 systems.The extra trees forest model outperforms the other models in the validation,with an ROC-AUC of 0.88.Our work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.
文摘The hydrological models and simpli?ed methods of Saint-venant equations are used extensively in hydrological modeling, in particular for the simulation of the ?ood routing. These models require speci?c and extensive data that usually makes the study of ?ood propagation an arduous practice. We present in this work a new model, based on a transfer function, this function is a function of parametric probability density, having a physical meaning with respect to the propagation of a hydrological signal. The inversion of the model is carried out by an optimization technique called Genetic Algorithm. It consists of evolving a population of parameters based primarily on genetic recombination operators and natural selection to?nd the minimum of an objective function that measures the distance between observed and simulated data. The precision of the simulations of the proposed model is compared with the response of the Hayami model and the applicability of the model is tested on a real case, the N'Fis basin river, located in the High Atlas Occidental, which presents elements that appear favorable to the study of the propagation. The results obtained are very satisfactory and the simulation of the proposed model is very close to the response of the Hayami model.
基金This work has been supported by the National Key R&D Program of China(Grant No.2018YFE0207600)EPSRC Research Grant(EP/K033700/1,EP/K033166/1)+2 种基金the Natural Science Foundation of China(61671046,61911530216,U1834210)the Beijing Natural Science Foundation(4182050)the FWO(Grants G0A2617N and G093817N).
文摘This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.
文摘Dear Editor,Middle East respiratory syndrome coronavirus（MERS-CoV）affected 1,621 patients worldwide,with a 36%mortality rate by the end of 2015.The highest number of cases was recorded in Saudi Arabia,
文摘Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for relevant patients with DoCs assessment, including brain-computer interfaces(BCIs). Recent progress in BCIs' clinical applications may offer important breakthroughs in the diagnosis and therapy of patients with DoCs. Thus the clinical significance of BCI applications in the diagnosis of patients with DoCs is hard to overestimate. One of them may be brain-computer interfaces. The aim of this study is to evaluate possibility of non-invasive EEG-based brain-computer interfaces in diagnosis of patients with DOCs in post-acute and long-term care institutions.
文摘In England and Wales,the Mental Health Act(MHA)1983 provides the legal framework for the detention of individuals suffering from a mental disorder if they are judged to present a risk of harm to self or others.The MHA removes from certain psychiatric patients civil liberties otherwise inherent in our legal system.Through both statute and common law,it balances a patient’s right to autonomy with psychiatrists'duty of care by reference to the health and safety of the patient.It also balances the civil rights of individual patients against the right of society to protection1.
文摘Notwithstanding the discovery of vaccines for Covid-19, the virus'srapid spread continues due to the limited availability of vaccines, especially inpoor and emerging countries. Therefore, the key issues in the presentCOVID-19 pandemic are the early identification of COVID-19, the cautiousseparation of infected cases at the lowest cost and curing the disease in the earlystages. For that reason, the methodology adopted for this study is imaging tools,particularly computed tomography, which have been critical in diagnosing andtreating the disease. A new method for detecting Covid-19 in X-rays and CTimages has been presented based on the Scatter Wavelet Transform and DenseDeep Neural Network. The Scatter Wavelet Transform has been employed as afeature extractor, while the Dense Deep Neural Network is utilized as a binaryclassifier. An extensive experiment was carried out to evaluate the accuracy ofthe proposed method over three datasets: IEEE 80200, Kaggle, andCovid-19 X-ray image data Sets. The dataset used in the experimental part consists of 14142. The numbers of training and testing images are 8290 and 2810,respectively. The analysis of the result refers that the proposed methods achievedhigh accuracy of 98%. The proposed model results show an excellent outcomecompared to other methods in the same domain, such as (DeTraC) CNN, whichachieved only 93.1%, CNN, which achieved 94%, and stacked Multi-ResolutionCovXNet, which achieved 97.4%. The accuracy of CapsNet reached 97.24%.
文摘Complex proteins are needed for many biological activities.Folding amino acid chains reveals their properties and functions.They support healthy tissue structure,physiology,and homeostasis.Precision medicine and treatments require quantitative protein identification and function.Despite technical advances and protein sequence data exploration,bioinformatics’“basic structure”problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved.Protein function inference from amino acid sequences is the main biological data challenge.This study analyzes whether raw sequencing can characterize biological facts.A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.A coding technique for each sequence in each family was devised using two representations to identify each amino acid precisely.A bispectral analysis converts encoded protein numerical sequences into images for better protein sequence and family discrimination.Training and validation employed 70%of the dataset,while 30%was used for testing.This paper examined the performance of multistage deep learning models for differentiating between sixteen protein families after encoding and representing each encoded sequence by a higher spectral representation image(Bispectrum).Cascading minimized false positive and negative cases in all phases.The initial stage focused on two classes(six groups and ten groups).The subsequent stages focused on the few classes almost accurately separated in the first stage and decreased the overlapping cases between families that appeared in single-stage deep learning classification.The single-stage technique had 64.2%+/-22.8%accuracy,63.3%+/-17.1%precision,and a 63.2%+/19.4%F1-score.The two-stage technique yielded 92.2%+/-4.9%accuracy,92.7%+/-7.0%precision,and a 92.3%+/-5.0%F1-score.This work provides balanced,reliable,and precise forecasts for all families in all measures.It ensured that the new model was resilient to family variances and provided high-scoring results.
文摘Proteins are essential for many biological functions.For example,folding amino acid chains reveals their functionalities by maintaining tissue structure,physiology,and homeostasis.Note that quantifiable protein characteristics are vital for improving therapies and precision medicine.The automatic inference of a protein’s properties from its amino acid sequence is called“basic structure”.Nevertheless,it remains a critical unsolved challenge in bioinformatics,although with recent technological advances and the investigation of protein sequence data.Inferring protein function from amino acid sequences is crucial in biology.This study considers using raw sequencing to explain biological facts using a large corpus of protein sequences and the Globin-like superfamily to generate a vector representation.The power of two representations was used to identify each amino acid,and a coding technique was established for each sequence family.Subsequently,the encoded protein numerical sequences are transformed into an image using bispectral analysis to identify essential characteristics for discriminating between protein sequences and their families.A deep Convolutional Neural Network(CNN)classifies the resulting images and developed non-normalized and normalized encoding techniques.Initially,the dataset was split 70/30 for training and testing.Correspondingly,the dataset was utilized for 70%training,15%validation,and 15%testing.The suggested methods are evaluated using accuracy,precision,and recall.The non-normalized method had 70%accuracy,72%precision,and 71%recall.68%accuracy,67%precision,and 67%recall after validation.Meanwhile,the normalized approach without validation had 92.4%accuracy,94.3%precision,and 91.1%recall.Validation showed 90%accuracy,91.2%precision,and 89.7%recall.Note that both algorithms outperform the rest.The paper presents that bispectrum-based nonlinear analysis using deep learning models outperforms standard machine learning methods and other deep learning methods based on convolutional architecture.They offered the best inference performance as the proposed approach improves categorization and prediction.Several instances show successful multi-class prediction in molecular biology’s massive data.
文摘Denial of Service(DoS/DDoS)intrusions are damaging cyberattacks,and their identification is of great interest to the Intrusion Detection System(IDS).Existing IDS are mainly based on Machine Learning(ML)methods including Deep Neural Networks(DNN),but which are rarely hybridized with other techniques.The intrusion data used are generally imbalanced and contain multiple features.Thus,the proposed approach aims to use a DNN-based method to detect DoS/DDoS attacks using CICIDS2017,CSE-CICIDS2018 and CICDDoS 2019 datasets,according to the following key points.a)Three imbalanced CICIDS2017-2018-2019 datasets,including Benign and DoS/DDoS attack classes,are used.b)A new technique based on K-means is developed to obtain semi-balanced datasets.c)As a feature selectionmethod,LDA(Linear Discriminant Analysis)performance measure is chosen.d)Four metaheuristic algorithms,counting Artificial Immune System(AIS),Firefly Algorithm(FA),Invasive Weeds Optimization(IWO)and Cuckoo Search(CS)are used,for the first time together,to increase the performance of the suggested DNN-based DoS attacks detection.The experimental results,based on semi-balanced training and test datasets,indicated that AIS,FA,IWO and CS-based DNNs can achieve promising results,even when cross-validated.AIS-DNN yields a tested accuracy of 99.97%,99.98%and 99.99%,for the three considered datasets,respectively,outperforming performance established in several related works.
