A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers

The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope(SEM)images of the electrospun nanofiber,to ensure that no structural defects are produced.The presence of anomalies prevents practical application of the electrospun nanofibrous material in nanotechnology.Hence,the automatic monitoring and quality control of nanomaterials is a relevant challenge in the context of Industry 4.0.In this paper,a novel automatic classification system for homogenous(anomaly-free)and non-homogenous(with defects)nanofibers is proposed.The inspection procedure aims at avoiding direct processing of the redundant full SEM image.Specifically,the image to be analyzed is first partitioned into subimages(nanopatches)that are then used as input to a hybrid unsupervised and supervised machine learning system.In the first step,an autoencoder(AE)is trained with unsupervised learning to generate a code representing the input image with a vector of relevant features.Next,a multilayer perceptron(MLP),trained with supervised learning,uses the extracted features to classify non-homogenous nanofiber(NH-NF)and homogenous nanofiber(H-NF)patches.The resulting novel AE-MLP system is shown to outperform other standard machine learning models and other recent state-of-the-art techniques,reporting accuracy rate up to92.5%.In addition,the proposed approach leads to model complexity reduction with respect to other deep learning strategies such as convolutional neural networks(CNN).The encouraging performance achieved in this benchmark study can stimulate the application of the proposed scheme in other challenging industrial manufacturing tasks.

Resilient Control in Large-Scale Networked Cyber-Physical Systems: Guest Editorial

RECENT advances in sensing,communication and computing have open the door to the deployment of largescale networks of sensors and actuators that allow fine-grain monitoring and control of a multitude of physical processes and infrastructures.The appellation used by field experts for these paradigms is Cyber-Physical Systems(CPS)because the dynamics among computers,networking media/resources and physical systems interact in a way that multi-disciplinary technologies(embedded systems,computers,communications and controls)are required to accomplish prescribed missions.Moreover,they are expected to play a significant role in the design and development of future engineering applications such as smart grids,transportation systems,nuclear plants and smart factories.

“Noisy”issues in road acoustics:A white paper

Despite almost a century of studies dealing with traffic noise,researchers and practitioners still face old and new issues when designing a low-noise pavement.Given that,this manuscript aims at focusing on a number of unsolved questions,namely theoretical or technological.1)Is it viable to balance diverse road-related needs(i.e.,noise,expected life,texture levels,and friction)?2)How much does the pavement material affect its acoustic performance(the remaining factors being constant)?3)How much reliable is the relationship between road texture and mixture aggregate gradation?Based on the analysis of these issues,it emerges that:1)optimal pavement design involves complex mix optimization and there are theoretical and practical bases to set up a balanced approach to address the complexity of pavement design;2)high percentages of crumb rubber could optimise road acoustic response but this latter has a relationship with the tyre/road noise(expressed,for example,in terms of close proximity index)that calls for further investigation;3)aggregate gradation appears to be a reliable basis to predict surface texture and therefore,under given boundary conditions,tyre/road noise;and 4)further studies and investigations are needed in terms of local calibration of deterioration curves and setting up of a sound method to assess the frequency response of asphalt concretes and to govern on-site noise indicators based on mixture properties.

Evaluating Group Formation in Virtual Communities

In this paper, we are interested in answering the following research question: "Is it possible to form effective groups in virtual communities by exploiting trust information without significant overhead, similarly to real user communities?"In order to answer this question, instead of adopting the largely used approach of exploiting the opinions provided by all the users of the community(called global reputation), we propose to use a particular form of reputation, called local reputation. We also propose an algorithm for group formation able to implement the proposed procedure to form effective groups in virtual communities. Another interesting question is how to measure the effectiveness of groups in virtual communities. To this aim we introduce the index in a measure of the effectiveness of the group formation. We tested our algorithm by realizing some experimental trials on real data from the real world EPINIONS and CIAO communities, showing the significant advantages of our procedure w.r.t. another prominent approach based on traditional global reputation.

An automated medium scale prototype for anaerobic co-digestion of olive mill wastewater

Olive oil production constitutes one of the most important agro-industrial business for Mediterranean countries,where 97% of the international production is focused.Such an activity,mainly carried out through three phase olive oil mill plants,generates huge amounts of solid and liquid by-products further than olive oil.Physico-chemical features of these by-products depend on various factors such as soil and climatic conditions,agricultural practices and processing.As currently carried out,the disposal of these byproducts may lead to numerous problems taking into account management,economic and particularly environmental aspects.Indeed,olive mill wastewater is not easily biodegradable due to its high chemical and biochemical oxygen demand,its high content in phenolic compounds,high ratio C/N and low pH,leading consequently to soil and water source pollution.Considering,the above-mentioned statements,olive mill waste disposal constitutes nowadays a challenge for oil industry stakeholders.It becomes necessary to look for alternative solutions in order to overcome environmental problems and ensure the sustainability of oil industry.Anaerobic co-digestion of olive mill wastewater with other agro-industrial matrices could be one of these solutions;since it offers the possibility to produce green energy and break down toxicological compounds contained in these wastewater for a better disposal of the digested matrices as soil conditioner.In this contest,this note reports the functioning principle of an automated medium scale plant for anaerobic co-digestion of olive mill wastewater.

Exploiting European GNSS and Ethereum in location proof systems

Location-Based Services(LBSs)are essential in many application contexts like ride-sharing or navigation apps.There are cases where users could gain an advantage by submitting fake locations.The problem faced in this paper concerns the possibility that the geographic location declared by a user is not the actual location in which the user is placed.Some solutions are based on centralized or distributed verification in the literature,and other solutions are based on witnesses or infrastructure.In this paper,we highlight the limitations of such approaches and propose a new scheme that exploits signals coming from satellites to provide trustworthy location proofs,also respecting users'privacy.The proposed approach is decentralized because location proofs are stored by users in a suitably-encrypted way,and a blockchain is adopted to guarantee data integrity and authenticity.We show that the proposed approach overcomes the state of the art through a detailed analysis.