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In this paper, to study the effect of dynamic and static interference of clearance flow in fluid machinery caused by changes in rotational speed, the model was simplified to a rotor-stator system cavity flow. Investigating the flow characteristics in the cavity by changing the rotor speed of the rotor-stator system is of considerable significance. ANSYS-CFX was applied to numerically simulate the test model and the results were compared with the experimental results of the windage torque of the rotor-stator system. The inlet flow rate and geometric model remained unchanged. With an increase in the rotating Reynolds number, the shear stress on the rotor wall gradually increased, and the maximum gradient was within l* &lt, 0.15. In addition to the shear stress, the tangential Reynolds stress Rrθ contributed partly to the torque on the rotor wall. The swirling vortex formed by entrainment in the cavity of the rotor-stator system tended to separate at ReΦ= 3.53 × 106. As the rotating Reynolds number continued to increase, the secondary vortex finally separated completely. The strength of the vortex in the rotor turbulent boundary layer decreased with an increase in the rotating speed, but the number of vortex cores increased with the increase of speed. Depending on the application of the fluid machine, controlling the rotating speed within a reasonable range can effectively improve the characteristics of the clearance flow.

Social media platforms are increasingly being used to communicate information, something which has only intensified during the pandemic. News portals and governments are also increasing attention to digital communications, announcements and response or reaction monitoring. Twitter, as one of the largest social networking sites, which has become even more important in the communication of information during the pandemic, provides space for a lot of different opinions and news, with many discussions as well. In this paper, we look at the sentiments of people and we use tweets to determine how people have related to COVID-19 over a given period of time. These sentiment analyses are augmented with information extraction and named entity recognition to get an even more comprehensive picture. The sentiment analysis is based on the ’Bidirectional encoder representations from transformers’ (BERT) model, which is the basic measurement model for the comparisons. We consider BERT as the baseline and compare the results with the RNN, NLTK and TextBlob sentiment analyses. The RNN results are significantly closer to the benchmark results given by BERT, both models are able to categorize all tweets without a single tweet fall into the neutral category. Then, via a deeper analysis of these results, we can get an even more concise picture of people’s emotional state in the given period of time. The data from these analyses further support the emotional categories, and provide a deeper understanding that can provide a solid starting point for other disciplines as well, such as linguistics or psychology. Thus, the sentiment analysis, supplemented with information extraction and named entity recognition analyses, can provide a supported and deeply explored picture of specific sentiment categories and user attitudes.

A new beam scanning method of a Fabry–Perot cavity (FPC) antenna is proposed. To obtain high gain in a target direction with a reduced sidelobe level (SLL), we devised a tapered partially reflective surface (PRS) as a superstrate. Moreover, to attain various beam scanning directions, a phase-controllable artificial magnetic conductor (AMC) ground plane with a broad reflection phase range and high reflection magnitudes was introduced. In the proposed method, a new formula to satisfy an FP resonance condition in a cavity for a scanned beam is also suggested. According to the formula, the FPC antenna can precisely scan the main beam in designed target directions with well-maintained high gain, which has been hardly achievable. In addition, our method demonstrates the potential of electrical beam-scanning antennas by employing active RF chips on the AMC cells. To validate the method, we fabricated a prototype FPC antenna for a scanned beam at θ = 30°. Furthermore, we conducted an additional simulation for a different beam scanning angle as well. Good agreement between the expected and experimental results verifies our design approach.

Background: The mechanisms of periimplantitis are mostly based on biofilm-induced inflammatory lesions around dental implants. However, the host reaction, reflected by the intensity of the inflammatory response to bacterial products, is crucial for peri-implant bone destruction. The aim of this pilot study was to measure total antioxidant status (TAS), the amount of IL-6, IL-8 and TNF-α (tumor necrosis factor), salivary lactate dehydrogenase (LDH) levels and the correlation with one-year peri-implant bone loss. Methods: Seven consecutive patients (Group 1), with the presence of at least one prior inserted dental implant, and three healthy individuals, fully dentate (Group 0), were enrolled. IL-6, IL-8, TNF-α, TAS and LDH were determined in saliva of both groups. Peri-implant marginal bone loss (MBL) at one year compared to baseline was evaluated for Group 1. Results: A statistically significant positive correlation was found in Group 1 between MBL and the IL-8 (p = 0.033) and a strong negative correlation between MBL and TAS (p = 0.002), but no statistically significant differences of the assessed parameters were found between the two groups. Conclusions: In the limits of the present preliminary study, salivary TAS and proinflammatory cytokines could be associated with the risk of peri-implant bone loss over time.

As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.

At present, the absolute positioning accuracy and control accuracy of industrial serial robots need to be improved to meet the accuracy requirements of precision manufacturing and precise control. An accurate dynamic model is an important theoretical basis for solving this problem, and precise dynamic parameters are the prerequisite for precise control. The research of dynamics and parameter identification can greatly promote the application of robots in the field of precision manufacturing and automation. In this paper, we study the dynamical modeling and dynamic parameter identification of an industrial robot system with six rotational DOF (6R robot system) and propose a new method for identifying dynamic parameters. Our aim is to provide an accurate mathematical description of the dynamics of the 6R robot and to accurately identify its dynamic parameters. First, we establish an unconstrained dynamic model for the 6R robot system and rewrite it to obtain the dynamic parameter identification model. Second, we establish the constraint equations of the 6R robot system. Finally, we establish the dynamic model of the constrained 6R robot system. Through the ADAMS simulation experiment, we verify the correctness and accuracy of the dynamic model. The experiments prove that the result of parameter identification has extremely high accuracy and the dynamic model can accurately describe the 6R robot system mathematically. The dynamic modeling method proposed in this paper can be used as the theoretical basis for the study of 6R robot system dynamics and the study of dynamics-based control theory.

This paper derives an original finite element for the static bending analysis of a transversely cracked uniform beam resting on a two-parametric elastic foundation. In the simplified computational model based on the Euler–Bernoulli theory of small displacements, the crack is represented by a linear rotational spring connecting two elastic members. The derivations of approximate transverse displacement functions, stiffness matrix coefficients, and the load vector for a linearly distributed load along the entire beam element are based on novel cubic polynomial interpolation functions, including the second soil parameter. Moreover, all derived expressions are obtained in closed forms, which allow easy implementation in existing finite element software. Two numerical examples are presented in order to substantiate the discussed approach. They cover both possible analytical solution forms that may occur (depending on the problem parameters) from the same governing differential equation of the considered problem. Therefore, several response parameters are studied for each example (with additional emphasis on their convergence) and compared with the corresponding analytical solution, thus proving the quality of the obtained finite element.

We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemination between multiple entities, our model implements the convolutional neural network (CNN) with fastText-based pre-trained embedding vectors, utilizes only the citation context as its input to distinguish between important and non-important citations. Moreover, we speculate using focal-loss and class weight methods to address the inherited class imbalance problems in citation classification datasets. Using a dataset of 10 K annotated citation contexts, we achieved an accuracy of 90.7% along with a 90.6% f1-score, in the case of binary classification. Finally, we present a case study to measure the comprehensiveness of our deployed model on a dataset of 3100 K citations taken from the ACL Anthology Reference Corpus. We employed state-of-the-art graph visualization open-source tool Gephi to analyze the various aspects of citation network graphs, for each respective citation behavior.

The health of trees has become an important issue in forestry. How to detect the health of trees quickly and accurately has become a key area of research for scholars in the world. In this paper, a living tree internal defect detection model is established and analyzed using model-driven theory, where the theoretical fundamentals and implementations of the algorithm are clarified. The location information of the defects inside the trees is obtained by setting a relative permittivity matrix. The data-driven inversion algorithm is realized using a model-driven algorithm that is used to optimize the deep convolutional neural network, which combines the advantages of model-driven algorithms and data-driven algorithms. The results of the comparison inversion algorithms, the BP neural network inversion algorithm, and the model-driven deep learning network inversion algorithm, are analyzed through simulations. The results shown that the model-driven deep learning network inversion algorithm maintains a detection accuracy of more than 90% for single defects or homogeneous double defects, while it can still have a detection accuracy of 78.3% for heterogeneous multiple defects. In the simulations, the single defect detection time of the model-driven deep learning network inversion algorithm is kept within 0.1 s. Additionally, the proposed method overcomes the high nonlinearity and ill-posedness electromagnetic inverse scattering and reduces the time cost and computational complexity of detecting internal defects in trees. The results show that resolution and accuracy are improved in the inversion image for detecting the internal defects of trees.

In-band full-duplex communication offers significant potential to enhance network performance. This paper presents the full-duplex linear transmit delay allocation MAC (FD-LTDA-MAC) protocol for full-duplex based underwater acoustic chain networks (FD-UACNs) for subsea pipeline monitoring. This incorporates a number of extensions to the LTDA-MAC protocol in order to fully exploit advantages of full-duplex communication to enhance the efficiency of underwater facility monitoring. The protocol uses a greedy optimisation algorithm to derive collision-free packet schedules for delivering data packets to the sink node of the underwater chain network. The purpose of this paper is to show the significant improvement that can be achieved in packet scheduling by exploiting temporal spectrum re-use of an underwater acoustic channel through full-duplex communication. Simulation results show that more efficient packet scheduling and reduced end-to-end packet delays can be achieved in large scale scenarios using FD-LTDA-MAC compared with LTDA-MAC and LTDA-MAC with full-duplex enabled nodes. It can provide much higher monitoring rates for long range underwater pipelines using low cost, mid range, low rate, and low power acoustic modems.

Indirect neutron imaging is an effective method for nondestructive testing of spent nuclear fuel elements. Considering the difficulty of obtaining experimental data in a high-radiation environment and the characteristic of high noise of neutron images, it is difficult to use the traditional FBP algorithm to recover the complete information of the sample based on the limited projection data. Therefore, it is necessary to develop the sparse-view CT reconstruction algorithm for indirect neutron imaging. In order to improve the quality of the reconstruction image, an iterative reconstruction method combining SIRT, MRP, and WTDM regularization is proposed. The reconstruction results obtained by using the proposed method on simulated data and actual neutron projection data are compared with the results of four other algorithms (FBP, SIRT, SIRT-TV, and SIRT-WTDM). The experimental results show that the SIRT-MWTDM algorithm has great advantages in both objective evaluation index and subjective observation in the reconstruction image of simulated data and neutron projection data.

This paper presents a plug-in electric vehicle (PEV) charging control algorithm, Adjustable Real-Time Valley Filling (ARVF), to improve PEV charging and minimize adverse effects from uncontrolled PEV charging on the grid. ARVF operates in real time, adjusts to sudden deviations between forecasted and actual baseloads, and uses fuzzy logic to deliver variable charging rates between 1.9 and 7.2 kW. Fuzzy logic is selected for this application because it can optimize nonlinear systems, operate in real time, scale efficiently, and be computationally fast, making ARVF a robust algorithm for real-world applications. In addition, this study proves that when the forecasted and actual baseload vary by more than 20%, its real-time capability is more advantageous than algorithms that use optimization techniques on predicted baseload data.

In this paper, monosaccharide (glucose and fructose), disaccharide (sucrose and trehalose), trisaccharide (raffinose) and tetrasaccharide (stachyose) were selected as the research objects and the glycosyl polyaldehydes and glycosyl polyuronic acid anti-wrinkle finishing agents were prepared via selective oxidation. The anti-wrinkle properties of their finished fabrics were analyzed, the molecular radius, the number of reactive groups, the number of reaction sites and the number of rotatable bonds of the finishing agent were calculated to evaluate its diffusion rate and crosslinking characteristics inside the cellulose. Through molecular simulation, the number of possible conformations of the anti-wrinkle finishing agent with the single cross-linked state was calculated, and the distance between different cross-linking points was measured, and the relationship between the effective cross-linking radius and the anti-wrinkle performance was studied. The results showed that trehalose polyaldehydes, raffinose polyaldehydes, trehalose polyuronic acid, and raffinose polyuronic acid finished fabrics had an excellent anti-wrinkle property, the strength retention rates of the fabrics were all above 68%, and the whiteness index was above 70. The smaller the molecular radius was, the easier the finishing agent was to diffuse into the cellulose. The most suitable crosslinking radius of glycosyl finishing agent was 3.5–6.0 Å.

Laser speckle imaging has been an indispensable tool for visualizing blood flow in biomedical applications. We proposed a novel design of the laser speckle imaging system, which combines confocal illumination and detection with various speckle analysis methods. The system can be operated by three imaging modes. One is surface illumination laser speckle contrast imaging (SI-LSCI) and the other two are line scan temporal speckle contrast imaging (LS-TSCI) and line scan spatial speckle contrast imaging (LS-SSCI). The experimental results of flow phantoms have validated the mixture model, which combines the Lorentzian and Gaussian models to describe the simultaneous existence of both Brownian motions and ordered flow. Our experimental results of in vivo chick embryos demonstrate that LS-SSCI maintains high temporal resolution and is less affected by motion artifacts. LS-SSCI can provide better image quality for in vivo imaging blood chick embryos than LS-TSCI. Furthermore, the experiential results present that LS-SSCI can detect and quantify the blood flow change during vascular clipping, and shows great potential in diagnosing vascular diseases, such as angiosclerosis, angiostenosis, or angiemphraxis.

Increases in power demand and consumption are very noticeable. This increase presents a number of challenges to the traditional grid systems. Thus, there is the need to come up with a new solution that copes with the stringent demand on energy and provides better power quality, which gives a better experience to the end users. This is how the concept of smart grids (SG) came to light. SGs have been introduced to better monitor and control the power produced and consumed. In addition to this, SGs help with reducing the electricity bill through the integration of renewable energy sources. The underlying smartness of the SGs resides in the flow of information in addition to the flow of energy. Information/data flowing implies the use of smart sensors and smart meters that sense and send data about the power produced and consumed, and the data about the environment where they are deployed. This makes SGs a direct application of IoT. In this paper, we are implementing an edge platform that is based on single-board computers (SBCs) to process data stemming from SG. The use of SBCs is driven by the energy efficiency and cost effectiveness concepts that the SG is trying to apply. The platform in question is tested against a distributed job that averages random numbers using Hadoop’s MapReduce programming model. The SBC that we are using in this implementation is the NVIDIA Jetson Developer Kit. The results of this work show that a cluster of SBCs is low-cost, easy to maintain, and simple to deploy, which makes it a great candidate for providing edge computing. Although it revealed a performance that beat the one of the remote cloud servers, it could not outperform the single-computer edge platform.

Nowadays, due to the complexity and design of many advanced structures, cylindrical shells are starting to have numerous applications. Therefore, the main goal of this work is to study the effect of thickness and the benefits of a carbon composite sandwich cylindrical shell incorporating a cork core, compared to a conventional carbon composite cylindrical shell, in terms of the static and impact performances. For this purpose, static and impact tests were carried out with the samples freely supported on curved edges, while straight edges were bi-supported. A significant effect of the thickness on static properties and impact performance was observed. Compared to thinner shells, the failure load on the static tests increased by 237.9% and stiffness by 217.2% for thicker shells, while the restored energy obtained from the impact tests abruptly increased due to the collapse that occurred for the thinner ones. Regarding the sandwich shells, the incorporation of a cork core proved to be beneficial because it promoted an increase in the restored energy of around 44.8% relative to the conventional composite shell. Finally, when a carbon skin is replaced by a Kevlar one (hybridization effect), an improvement in the restored energy of about 20.8% was found. Therefore, it is possible to conclude that numerous industrial applications can benefit from cylindrical sandwiches incorporating cork, and their hybridization with Kevlar fibres should be especially considered when they are subject to impact loads. This optimized lay-up is suggested because Kevlar fibres fail through a series of small fibril failures, while carbon fibres exhibit a brittle collapse.

With the complexity of the task requirement, multiple operating conditions have gradually become the common scenario for equipment. However, the degradation trend of monitoring data cannot be accurately extracted in life prediction under multiple operating conditions, which is because some monitoring data is affected by the operating conditions. Aiming at this problem, this paper proposes an improved similarity trajectory method that can directly use the monitoring data under multiple operating conditions for life prediction. The morphological pattern and symbolic aggregate approximation-based similarity measurement method (MP-SAX) is first used to measure the similarity between the monitoring data under multiple operating conditions. Then, the similar life candidate set, and corresponding weight are obtained according to the MP-SAX. Finally, the life prediction results of equipment under multiple operating conditions can be calculated by aggregating the similar life candidate set. The proposed method is validated by the public datasets from NASA Ames Prognostics Data Repository. The results show that the proposed method can directly and effectively use the original monitoring data for life prediction without extracting the degradation trend of the monitoring data.

At the core of augmented reality audio (ARA) technology lies the ARA mix, a process responsible for the assignment of a virtual environment to a real one. Legacy ARA mix models have focused on the natural reproduction of the real environment, whereas the virtual environment is simply mixed through fixed gain methods. This study presents a novel approach of a dynamic ARA mix that facilitates a smooth adaptation of the virtual environment to the real one, as well as dynamic control of the virtual audio engine, by taking into account the inherent characteristics of both ARA technology and binaural auditory perception. A prototype feature extraction technique of auditory perception characteristics through a real-time binaural loudness prediction method was used to upgrade the legacy ARA mix model into a dynamic model, which was evaluated through benchmarks and subjective tests and showed encouraging results in terms of functionality and acceptance.

Cu/Zr-modified TiO2 photocatalysts were prepared in the form of nanopowders and characterized by photothermal spectrometry, UV–Vis spectrophotometry and X-ray diffraction (XRD) to investigate the effect of Cu/Zr content on their thermo-optical and transport properties. Adding Cu (0.05%) caused a change in the light absorption range limit, which reduced from 3.25 eV for pure TiO2 to 2.85 eV for Cu-modified TiO2. The decrease in energy band gap was accompanied by a 19.5% decrease in the charge carrier lifetime, which is not favorable for photocatalysis. The decrease in charge carrier lifetime can be minimized by additional modification of TiO2 with Zr (1%), which showed insignificant effects on the energy band gap of the investigated materials. Furthermore, modification of TiO2 with Zr affected the material’s structure and increased its specific surface area, which improved the adsorption of degraded compounds as well as the absorption of light. Altogether, these effects resulted in higher photocatalytic degradation rate constants of the investigated TiO2-based photocatalyst. It was also found that modification of TiO2 with Cu and/or Zr increases both the material’s thermal diffusivity and conductivity due to changes in the band gap and structure of material. Beam deflection spectrometry (BDS) has demonstrated high potential in materials’ characterization which stems from its high sensitivity and precision.

Estimation of the critical latency that can cause collision in remote maneuvering of autonomous ships can provide a clue to avoid collisions. The concept of estimating the critical latency was established using the turning circle formed by the turning maneuver of the own ship, and critical latency was estimated using the radius of the turning circle with the turning time ratio. The turning circle was observed using the turning trajectory of the give-way vessel measured in the ship maneuvering simulation experiment. Experimental results demonstrated that the proposed method is capable of identifying both the location and time of the collision due to critical latency. As a result, a clue to avoid possible collision in remote maneuvering caused by critical latency was deduced.

With the world population set to approach an estimated 9 billion by 2050, against a background of finite natural resources, we need renewable biological resources for securing healthy food and animal feed to meet the nutritional requirements of the growing population. In this paper, key findings from ReValue Eranet project financed by Norway, India, and Spain are presented. The project aims to contribute to the UN Sustainable Development Goals (SDG) target on food losses reduction, by developing innovative technologies for the surimi industry, namely reducing losses by improved cold chain management and efficient conversion of rest raw materials (RRM) and wash water into value-added protein and oil ingredients for food and feed applications. A multidisciplinary research approach was applied with expertise from supply chain management, life cycle assessment, biotechnology, energy, and process engineering to propose several solutions for improving the overall resource efficiency of the surimi supply chains in India. This paper presents a synthesis of proposed solutions from ReValue project and potential contribution towards SDGs as well as market exploitation strategies.

The high costs and extensive time needed for the treatment of critical-sized bone defects are still major clinical concerns in orthopedic surgery, therefore, researchers continue to look for more cost and time-effective methods. This study aims to investigate the effects of a decellularized omentum scaffold with a combination of platelet-rich plasma (PRP) and mesenchymal stem cells on the healing of critical-sized bone defects. Wistar albino rats (n = 30) were investigated in five groups. Critical-sized bone defects were formed on bilateral radius shafts. No scaffold, decellularized omentum, omentum with PRP and omentum + mesenchymal stem cells was used in group 1 (control group), 2, 3 and 4, respectively. In addition, omentum with a combination of mesenchymal stem cells +PRP was used in group 5. After 6 weeks, both radiological and histological healing were evaluated comparatively among the groups. After the use of a decellularized omentum scaffold, vitality of new cells was maintained, and new bone formation occurred. When compared to the control group, radiological healing was significantly better (p = 0.047) in the omentum and omentum + PRP-treated groups. Furthermore, histological healing was better in the omentum and omentum + PRP-treated groups than the control group (p = 0.001). The use of a decellularized omentum scaffold is suitable in the healing of critical bone defects.

Chronodisruption deteriorates the health and wellbeing of shift workers. Artificial light at night and the lack of light during the day are major contributors to chronodisruption and need to be optimized in shift work scenarios. Here, we present one solution for a lighting and automation system in an industrial production workplace. The setting is a rapidly rotating shift work environment with morning, evening, and night shifts. We describe a procedure to specify the new lighting through a software-agnostic nonvisual lighting simulation for artificial and daylighting scenarios. Through this process, a new luminaire is created, called Drosa, that allows for a large melanopic stimulus range between 412 and 73 lx melanopic equivalent daylight (D65) illuminance vertically at eye level, while maintaining a neutral white illuminance at task level between 1250 and 900 lx, respectively. This is possible through a combination of glare-free spotlights with adjustable areal wing lights. An individually programmed automation system controls the light dosage and timing during the day and night. The work is relevant for other shift work scenarios, where the presented example and the discussed rationale behind the automation might provide insights. The work is further relevant for other lighting scenarios beyond industrial shift work, as the nonvisual lighting simulation process can be adapted to any context.

Estimating the bearing capacity of piles is an essential point when seeking for safe and economic geotechnical structures. However, the traditional methods employed in this estimation are time-consuming and costly. The current study aims at elaborating a new alternative model for predicting the pile-bearing capacity based on eleven new advanced machine-learning methods in order to overcome these limitations. The modeling phase used a database of 100 samples collected from different countries. Additionally, eight relevant factors were selected in the input layer based on the literature recommendations. The optimal inputs were modeled using the machine-learning methods and their performance was assessed through six performance measures using a K-fold cross-validation approach. The comparative study proved the effectiveness of the DNN model, which displayed a higher performance in predicting the pile-bearing capacity. This elaborated model provided the optimal prediction, i.e., the closest to the experimental values, compared to the other models and formulae proposed by previous studies. Finally, a reliable and easy-to-use graphical interface was generated, namely “BeaCa2021”. This will be very helpful for researchers and civil engineers when estimating the pile-bearing capacity, with the advantage of saving time and money.

Electrospray ionisation (ESI) is renowned for its ability to ionise intact proteins for sensitive detection by mass spectrometry (MS). However, the use of a conventional direct current ESI voltage can result in the formation of relatively large initial droplet sizes, which can limit efficient ion desolvation and sensitivity. Here, pulsed nanoESI (nESI) MS using nanoscale emitters with inner diameters of ~250 nm is reported. In this approach, the nESI voltage is rapidly pulsed from 0 to ~1.5 kV with sub-nanosecond rise times, duty cycles from 10 to 90%, and repetition rates of 10 to 350 kHz. Using pulsed nESI, the performance of MS for the detection of intact proteins can be improved in terms of increased ion abundances and decreased noise. The absolute ion abundances and signal-to-noise levels of protonated ubiquitin, cytochrome C, myoglobin, and carbonic anhydrase II formed from standard denaturing solutions can be increased by up to 82% and 154% using an optimal repetition rate of ~200 kHz compared to conventional nESI-MS. Applying pulsed nESI-MS to a mixture of four proteins resulted in the signal for each protein increasing by up to 184% compared to the more conventional nESI-MS. For smaller ions (≤1032 m/z), the signal can also be increased by the use of high repetition rates (200–250 kHz), which is consistent with the enhanced performance depending more on general factors associated with the ESI process (e.g., smaller initial droplet sizes and reduced Coulombic repulsion in the spray plume) rather than analyte-specific effects (e.g., electrophoretic mobility). The enhanced sensitivity of pulsed nESI is anticipated to be beneficial for many different types of tandem mass spectrometry measurements.

Neutralization with liquid digestate and CO2 was compared herein to adjust the pH of lime-treated corn stover. The effects on the thermophilic (55 °C) anaerobic digestion were also analyzed. Liquid digestate neutralization (LDN) caused a decrease in pH from 10.5 to 7.5 in 60 h and accumulation of acetic/isobutyric acids. The CO2 neutralization (CN) under solid-state conditions reduced the pH from 10.5 to 8.5 in 30 min, which is faster than that of LDN and did not affect the subsequent anaerobic digestion. Biomethane production rate indicates that LDN contributed to the performance of anaerobic digestion, but this was not sufficient to compensate for the loss of total biomethane yield, resulting in a negative net profit (i.e., revenue from increased energy production minus reagent cost). For CN under solid-state conditions, the biomethane production was highest in both liquid- and solid-state anaerobic digestion, and also obtained a net profit of 98.74–100.89 RMB/tonne dry biomass. Therefore, the solid-state condition CN is a more efficient and economic method for adjusting initial pH of lime-treated corn stover.

The estimation of the network traffic state, its likely short-term evolution, the prediction of the expected travel times in a network, and the role that mobility patterns play in transport modeling is usually based on dynamic traffic models, whose main input is a dynamic origin–destination (OD) matrix that describes the time dependencies of travel patterns, this is one of the reasons that have fostered large amounts of research on the topic of estimating OD matrices from the available traffic information. The complexity of the problem, its underdetermination, and the many alternatives that it offers are other reasons that make it an appealing research topic. The availability of new traffic data measurements that were prompted by the pervasive penetration of information and communications technology (ICT) applications offers new research opportunities. This study focused on GPS tracking data and explored two alternative modeling approaches regarding how to account for this new information to solve the dynamic origin–destination matrix estimation (DODME) problem, either including it as an additional term in the formulation model or using it in a data-driven modeling method to propose new model formulations. Complementarily, independently of the approach used, a key aspect is the quality of the estimated OD, which, as recent research has made evident, is not well measured by the conventional indicators. This study also explored this problem for the proposed approaches by conducting synthetic computational experiments to control and understand the process.

This study describes a contactless vital sign monitoring (CVSM) system capable of measuring heart rate (HR) and respiration rate (RR) using a low-power, indirect time-of-flight (ToF) camera. The system takes advantage of both the active infrared illumination as well as the additional depth information from the ToF camera to compensate for the motion-induced artifacts during the HR measurements. The depth information captures how the user is moving with respect to the camera and, therefore, can be used to differentiate where the intensity change in the raw signal is from the underlying heartbeat or motion. Moreover, from the depth information, the system can acquire respiration rate by directly measuring the motion of the chest wall during breathing. We also conducted a pilot human study using this system with 29 participants of different demographics such as age, gender, and skin color. Our study shows that with depth-based motion compensation, the success rate (system measurement within 10% of reference) of HR measurements increases to 75%, as compared to 35% when motion compensation is not used. The mean HR deviation from the reference also drops from 21 BPM to −6.25 BPM when we apply the depth-based motion compensation. In terms of the RR measurement, our system shows a mean deviation of 1.7 BPM from the reference measurement. The pilot human study shows the system performance is independent of skin color but weakly dependent on gender and age.

Diazinon exposures have been linked to the onset of toxic pathways and adverse outcomes in aquatic species, but the ecological implications on model species are not widely emphasized. The objective of this study was to determine how the organophosphate pesticide diazinon affected hematological (hemoglobin, total red blood count, total white blood count, and mean corpuscular hemoglobin), growth (condition factor, hepatosomatic index, specific growth rate), biochemical (total serum glucose, total serum protein), and endocrine (growth hormone, tri-iodothyronine, and thyroxine) parameters in Clarias batrachus after chronic exposure. Diazinon was administered at predefined exposure doses (0.64 and 1.28 mg/L) and monitored at 15, 30, and 45 days into the investigation. Observation for most biomarkers revealed patterns of decreasing values with increasing toxicant concentration and exposure duration. Correlation analysis highlighted a significant inverse relationship between variables (mean corpuscular hemoglobin, condition factor, specific growth rate, tri-iodothyronine, thyroxine, and total serum protein) and elevated chronic diazinon exposure concentrations. The integrated indices (IBR and BRI) indexes were used to provide visual and understandable depictions of toxicity effects and emphasized the relativity of biomarkers in terms of sensitivity and magnitude or severity of responses under graded toxicant exposures. The significant damage reflected by evaluated parameters in diazinon exposure groups compared to control portends risks to the health of local fish populations, including Clarias batrachus in aquatic systems adjacent to agrarian landscapes.

Objective: The goal of this study was to utilize Gait Profile Score (GPS) and Gait Deviation Index (GDI), to assess its capability of differentiating between injured and non-injured runners. Design: In total, 45 long-distance runners (15 non-injured, 30 injured), diagnosed with one of the following running related injuries, patella femoral pain syndrome, iliotibial pain syndrome, and medial tibial stress syndrome, were recruited. Methods: Data were obtained from a running analysis gait laboratory equipped with eight infrared motion-capturing cameras and a conventional treadmill. Running kinematics were recorded according to the Plug-In Gait model, measuring running deviations of the pelvis and lower extremities at a sampling rate of 200 Hz. GPS and GDI were calculated integrating pelvis and lower limb kinematics. Movement Analysis Profile results were compared between injured and non-injured runners. The non-parametric two-sample Wilcoxson test determined whether significant kinematic differences were observed. Results: Total GPS score significantly differed between the injured and non-injured runners. Not all running kinematics expressed by GDI differed between groups. Conclusions: GPS score was capable of discriminating between the injured and non-injured runners’ groups. This new running assessment method makes it possible to identify running injuries using a single numerical value and evaluate movements in individual joints.

The influence of modifier oxides (TiO2, ZnO, BaO, and PbO) on the mechanical and radiation shielding properties of boro-tellurate glasses is investigated. Samples with a composition of B2O3-SrO-TeO2-RO (RO represents the modifier oxides) were fabricated using the melt quench method, and their physical, mechanical, and radiation attenuation parameters were reported. For this aim, Monte Carlo simulation was employed to predict the radiation attenuation parameters, while the Makishima-Mackenzie model was adopted to determine the mechanical properties. The tightly packed structure with better cross-linkage density is possessed by the Ti-containing glass (SBT-Ti) system among the titled glass batch. The higher Poisson and micro-hardness values of the SBT-Ti glass indicate its structure’s reduced free volume and better compactness. For the glass with PbO, the linear and mass attenuation coefficients are highly increased compared to those glasses doped with TiO2, ZnO, and BaO. The thinner half-value layer was reported at 0.015 MeV, taking values 0.006, 0.005, 0.004, and 0.002 for samples with TiO2, ZnO, BaO, and PbO, respectively. SBT-Pb sample (with PbO) has a thinner HVL compared to other fabricated glass samples. The fabricated glasses’ thickness (Deq) equivalent to 1 cm of lead (Pb) was reported. The results demonstrated that Deq is high at low energy and equals 11.62, 8.81, 7.61, 4.56 cm for SBT-Ti, SBT-Zn, SBT-Ba, and SBT-Pb glass samples, respectively. According to the Deq results, the fabricated glasses have a shielding capacity between 30 and 43% compared to the pure Pb at gamma-ray energy of 1.5 MeV. At high energy (8 MeV), the transmission factor values for a thickness of 1 cm of the fabricated samples reach 88.68, 87.83, 85.95, and 83.11% for glasses SBT-Ti, SBT-Zn, SBT-Ba, and SBT-Pb, respectively. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. The authors acknowledge the support of Taif University Researchers Supporting Project number (TURSP-2020/127), Taif University, Taif, Saudi Arabia.

Particle velocity has been introduced to improve the performance of spatial sound field reproduction systems with an irregular loudspeaker array setup. However, existing systems have only been developed in the frequency domain. In this work, we propose a time-domain sound field reproduction method with both sound pressure and particle velocity components jointly controlled. To solve the computational complexity problem associated with the multi-channel setup and the long-length filter design, we adopt the general eigenvalue decomposition-based approach and the conjugate gradient method. The performance of the proposed method is evaluated through numerical simulations with both a regular loudspeaker array layout and an irregular loudspeaker array layout in a room environment.

There have been large developments in the unmanned aerial vehicles (UAV) industry over the last decade. Although UAV development was mainly for military related use in the beginning and despite there being fear surrounding the release of this technology to the open market for quite a long time, nowadays, there are a variety of applications where UAVs are used extensively, such as in agriculture, infrastructure inspection and monitoring, mobile retranslation relays for communications, etc. One of the weaknesses of electrically propelled UAVs is flight autonomy, there is often a difficult trade-of between the weight of the payload, batteries, and surface to be surveyed that is necessary to determine. There have been many attempts to use photovoltaic cells to increase the flight time for UAVs, however, a reliable solution has not yet been developed. The present paper presents improvements that have been conducted to extend the autonomy of electrically derived UAVs: instead of gluing photovoltaic cells on the wings, the new approach embeds the solar cells into the wing structure as well as develops a new wing that is significantly lighter to compensate for the weight added by the photovoltaic cells. It was demonstrated that by using this approach, a 33% increase in the flight time can be achieved with only one modified wing in a prototype vehicle.

In light of digitalisation, we are witnessing an increased volume of collected data and data generation and exchange acceleration. Therefore, the European Union (EU) has introduced the General Data Protection Regulation (GDPR) as a new framework for data protection on the European level. However, GDPR allows the member states to change some parts of the regulation, and the member states can always build on top of the GDPR. An example is the collection of biometric data with electronic signatures. This paper aims to compare the legislation on data protection topics in the various EU member states. The findings show that the member states included in the study generally do not have many additional/specific laws (only in 29.4% of the cases). However, almost all have other/additional legislation to the GDPR on at least one topic. The most additional legislation is on the topics of video surveillance, biometry, genetic data and health data. We also introduce a dynamic map that allows for quick navigating between different information categories and comparisons of the EU member states at a glance.

Polychromatic and wavelength-selective neutron transmission radiography were applied during bead-on-plate welding on 5 mm thick sheets on the face side of martensitic low transformation temperature (LTT) steel plates using gas tungsten arc welding (GTAW). The in situ visualization of austenitization upon welding and subsequent α’-martensite formation during cooling could be achieved with a temporal resolution of 2 s for monochromatic imaging using a single neutron wavelength and of 0.5 s for polychromatic imaging using the full spectrum of the beam (white beam). The spatial resolution achieved in the experiments was approximately 200 µm. The transmitted monochromatic neutron beam intensity at a wavelength of λ = 0.395 nm was significantly reduced during cooling below the martensitic start temperature Ms since the emerging martensitic phase has a ~10% higher attenuation coefficient than the austenitic phase. Neutron imaging was significantly influenced by coherent neutron scattering caused by the thermal motion of the crystal lattice (Debye–Waller factor), resulting in a reduction in the neutron transmission by approx. 15% for monochromatic and by approx. 4% for polychromatic imaging.

This article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, the machine learning prediction models and Explainable AI methods (XAI) serve as a base for the decision support system for smart manufacturing. The discovered information about the root causes behind the predicted failure can be used to improve the quality, and it also enables the definition of suitable security boundaries for better settings of the production parameters. The user’s need defines the given type of information. The developed method is applied to the monitoring of the surface roughness of the stainless steel strip, but the framework is not application dependent. The modeling analysis reveals that the parameters of the annealing and pickling line (RAP) have the best potential for real-time roughness improvement.

[EN] The links between emotions and rationality have been extensively studied and discussed. Several computational approaches have also been proposed to model these links. However, is it possible to build generic computational approaches and languages so that they can be "adapted " when a specific affective phenomenon is being modeled? Would these approaches be sufficiently and properly grounded? In this work, we want to provide the means for the development of these generic approaches and languages by making a horizontal analysis inspired by philosophical and psychological theories of the main affective phenomena that are traditionally studied. Unfortunately, not all the affective theories can be adapted to be used in computational models; therefore, it is necessary to perform an analysis of the most suitable theories. In this analysis, we identify and classify the main processes and concepts which can be used in a generic affective computational model, and we propose a theoretical framework that includes all these processes and concepts that a model of an affective agent with practical reasoning could use. Our generic theoretical framework supports incremental research whereby future proposals can improve previous ones. This framework also supports the evaluation of the coverage of current computational approaches according to the processes that are modeled and according to the integration of practical reasoning and affect-related issues. This framework is being used in the development of the GenIA(3) architecture., This work is partially supported by the Spanish Government projects PID2020-113416RB-I00, GVA-CEICE project PROMETEO/2018/002, and TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.

The increasing air pollution taking place in virtue of human activity has a novel impact in our health. Heterogeneous photocatalysis is a promising way of degrading volatile organic compounds (VOCs) that makes the quest of new and improved photocatalysts of great importance. Herein, perovskite-related materials ATiO3 with A = Mg, Ni, Co, Zn were synthesized through an ethylene glycol-mediated root, with ethylene glycol being used as a solvent and ligand. Characterization techniques such as X-ray diffraction (XRD), scanning electron microscopy, and energy dispersive X-ray spectroscopy (SEM/EDX), transmission electron microscopy (TEM), UV-vis spectroscopy, Raman spectroscopy, Fourier transform infrared (FT-IR), and photoluminescence spectroscopy (PL) were used in order to confirm the structure, the nanorod morphology, their absorption in UV-vis, and the separation efficiency of photogenerated charge carriers. The highest photoactivity was observed for ZnTiO3 in which 62% of toluene was decomposed after 60 min under LED illumination (54 mW/cm2).

Aiming at the mechanical response of geomembrane (GEM) in membrane-faced rockfill dam (MFRD) to different ambient temperatures, the mechanical properties in axial tension of polyvinyl chloride (PVC) GEM were studied by experiment and theoretical analysis. First, fifteen groups of axial tensile tests for longitudinal/transverse specimens were conducted at different temperatures in the temperature environment laboratory, the stress–strain curve and Young’s modulus were obtained, and the variation of Young’s modulus with temperature was analyzed by Boltzmann function fitting. Second, the glass transition temperature of PVC GEM was obtained by differential scanning calorimetry (DSC), and the difference in mechanical properties between longitudinal and transverse specimens of PVC GEM was analyzed by thermomechanical analyzer (TMA) thermodynamic test. The results showed that the lower the temperature, the greater the Young’s modulus, and the smaller the linear interval of stress and strain, while the higher the temperature, the result is opposite. The difference in mechanical properties between the two directions is related to the ambient temperature. The orientation of polymer structure accounts for the difference in mechanical properties by theoretical analysis. The fitting results of Boltzmann function have a certain reference value for numerical simulation. In design of the membrane impervious structure in MFRD, the ambient temperature should be considered fully, and the longitudinal/transverse welding splicing should be avoided as far as possible. The current test specification should test the mechanical performance of GEM at normal operating temperature of reservoir instead of the test and quality evaluation at a single temperature. The temperature should be considered comprehensively in construction to avoid damaging the performance of impervious structure and ensure the service life.

Mining enterprises are widely introducing digital technologies and automation is one of such tools. Granularity monitoring, namely, the size determination of rock mass pieces is a common operational component of the processes that extract minerals by open-pit mining. The article proposes an approach that, in addition to the lump size distribution, makes it possible to estimate the lump form distribution as well. To investigate the effectiveness of monitoring the form of blasted rock mass lumps, the authors conducted experiments in four stages related to the rock condition. They include geological occurrence, explosive crushing, trommelling, and mill crushing. The relationship between these stages is presented and the change in the lumps fragment form is traced. The present article proposes an informational and analytical model of the processes at mining enterprises, extracting minerals by open-pit mining, as well as an algorithm for determining the lumps form and obtaining their distribution in the rock mass.

This paper presents a novel meta-functional auxetic unit (MFAU) cell designed to improve performance and weight ratio for structural bridge bearing applications. Numerical investigations were conducted using three-dimensional finite element models validated by experimental results. The validated models were exposed to compression and buckling actions to identify structural failure modes, with special attention placed on the global behaviours of the meta-functional auxetic (MFA) composite bridge bearing. This bearing uses an unprecedented auxetic sandwich core design consisting of multiple MFAU cells. Numerical predictions of the elastic local critical buckling loads of the MFAU cell were in excellent agreement with both the analytical and experimental results, with an observed discrepancy of less than 1%. These results demonstrate that local buckling failures of MFAU cells can potentially be incurred prior to yielding under compression due to their slenderness ratios. Surprisingly, the designed sandwich core used in the MFA composite bridge bearing model can mimic an auxetic structure with significant crashworthiness, implying that this novel core composite structure can be tailored for structural bridge bearing applications. Parametric studies were thus carried out in order to enrich our insight into the MFA composite elements. These insights, stemming from both experimental and numerical studies, enable a novel design paradigm for MFAU that can significantly enhance the structural performance of MFA composite bridge bearings in practice.

Teleportation is an important protocol in quantum communication. Realizing teleportation between arbitrary nodes in multi-hop quantum networks is of great value. Most of the existing multi-hop quantum networks are based on Bell states or Greeberger–Horne–Zeilinger (GHZ) states. Bell state is more susceptible to noise than GHZ states after purification, but generating a GHZ state consumes more basic states. In this paper, a new quantum multi-hop network scheme is proposed to improve the interference immunity of the network and avoid large consumption at the same time. Teleportation is realized in a network based on entanglement swapping, fusion, and purification. To ensure the robustness of the system, we also design the purification algorithm. The simulation results show the successful establishment of entanglement with high fidelity. Cirq is used to verify the network on the Noisy Intermediate-Scale Quantum (NISQ) platform. The robustness of the fusion scheme is better than the Bell states scheme, especially with the increasing number of nodes. This paper provides a solution to balance the performance and consumption in a multi-hop quantum network.

Due to the difficulty of obtaining statistical energy parameters of complex structures and the complexity of modeling connection and model verification, the hybrid FE-SEA model has many problems in modeling complex structures. Therefore, in order to solve the above problems, this paper provides a reference for the application of the hybrid FE-SEA model in complex structures. In this paper, the hybrid FE-SEA commercial vehicle model is established by an experimental statistical energy parameter modeling method and a modification method. The model division and subsystem connection modeling of a complex substructure of a heavy vehicle cab are studied. In the hybrid model, the hybrid line connection and the hybrid point connection are established. On this basis, the parameters of the cab model were studied, and the statistical energy parameters such as modal density, internal loss factor, and coupling loss factor were obtained by the experimental method. The statistical energy parameters of the cab acoustic model are modified. Finally, the accuracy of the model is verified by vehicle test. In addition, the acoustic performance of the cab was optimized, and airtightness and acoustic packaging were verified. The full parameter modeling and correction method is adopted in this paper, which is an effective supplement to the traditional statistical energy parameter modeling method.

Full-matrix capture (FMC)-based ultrasonic imaging provides good sensitivity to small defects in non-destructive testing and has gradually become a mainstream research topic. Many corresponding algorithms have been developed, e.g., the total focusing method (TFM). However, the efficiency of the TFM is limited, especially in multi-layered structures. Although the appearance of wavenumber algorithms, such as extended phase-shift migration (EPSM) methods, has improved imaging efficiency, these methods cannot be applied to cases with oblique incidence. Therefore, a modified wavenumber method for full-matrix imaging of multi-layered structures with oblique array incidence is proposed. This method performs a coordinate rotation in the frequency domain to adapt it to the oblique incidence. It then utilizes wave-field extrapolation to migrate the transmitting and receiving wave field to each imaging line, and a correlation imaging condition is used to reconstruct a total focused image. The proposed method can deal with any incident angle without precision loss. Moreover, it inherits the computational efficiency advantages of the wavenumber algorithms. The simulation and experimental results show that the proposed method performs better in terms of accuracy and efficiency than the TFM. Specifically, it is nearly 60 times faster than the TFM when processing an FMC dataset with a size of 4096 × 64 × 64.

This paper presents an approach to compensate for the effect of thermal expansion on the structure of an industrial robot and thus to reduce the repeatability difference of the robot in cold and warm conditions. In contrast to previous research in this area that deals with absolute accuracy, this article is focused on determining achievable repeatability. To unify and to increase the robot repeatability, the measurements with highly accurate sensors were performed under different conditions on an industrial robot ABB IRB1200, which was equipped with thermal sensors, mounted on a pre-defined position around joints. The performed measurements allowed to implement a temperature-based prediction model of the end effector positioning error. Subsequent tests have shown that the implemented model used for the error compensation proved to be highly effective. Using the methodology presented in this article, the impact of drift can be reduced by up to 89.9%. A robot upgraded with a compensation principle described in this article does not have to be warmed up as it works with the same low repeatability error in the entire range of the achievable temperatures. Web of Science 11 22 art. no. 10813

In a frequency hopping spread spectrum (FHSS) network, the hopping pattern plays an important role in user authentication at the physical layer. However, recently, it has been possible to trace the hopping pattern through a blind estimation method for frequency hopping (FH) signals. If the hopping pattern can be reproduced, the attacker can imitate the FH signal and send the fake data to the FHSS system. To prevent this situation, a non-replicable authentication system that targets the physical layer of an FHSS network is required. In this study, a radio frequency fingerprinting-based emitter identification method targeting FH signals was proposed. A signal fingerprint (SF) was extracted and transformed into a spectrogram representing the time–frequency behavior of the SF. This spectrogram was trained on a deep inception network-based classifier, and an ensemble approach utilizing the multimodality of the SFs was applied. A detection algorithm was applied to the output vectors of the ensemble classifier for attacker detection. The results showed that the SF spectrogram can be effectively utilized to identify the emitter with 97% accuracy, and the output vectors of the classifier can be effectively utilized to detect the attacker with an area under the receiver operating characteristic curve of 0.99.

The task of charge prediction is to predict the charge based on the fact description. Existing methods have a good effect on the prediction of high-frequency charges, but the prediction of low-frequency charges is still a challenge. Moreover, there exist some confusing charges that have relatively similar fact descriptions, which can be easily misjudged. Therefore, we propose a model with data augmentation and feature augmentation for few-shot charge prediction. Specifically, the model takes the text description as the input and uses the Mixup method to generate virtual samples for data augmentation. Then, the charge information heterogeneous graph is introduced, and a novel graph convolutional network is designed to extract distinguishability features for feature augmentation. A feature fusion network is used to effectively integrate the charge graph knowledge into the fact to learn semantic-enhanced fact representation. Finally, the semantic-enhanced fact representation is used to predict the charge. In addition, based on the distribution of each charge, a category prior loss function is designed to increase the contribution of low-frequency charges to the model optimization. The experimental results on real-work datasets prove the effectiveness and robustness of the proposed model.

Targeting the challenge of determining the degree of blockage in buried pipelines and the difficulty of effectively extracting blockage features, a blockage detection method integrating variational mode decomposition (VMD) and information gain is proposed. Acoustic impulse response signals were obtained by deconvolving the output signals of the system, which were then subjected to VMD to obtain 12 components in different frequency ranges. Next, information gain (IG) was introduced to characterize the 12 components quantitatively, through which the components containing rich information about the pipe conditions were selected out. Meanwhile, sound pressure level conversion was performed on the selected components to amplify any changes in the sound field. Finally, the root mean square entropy (RMSE) was calculated to constitute the feature eigenvectors, which were input into Random Forests (RF) classifier for defect identification of pipeline. As the experimental results demonstrate, the proposed method is capable of determining the degree of blockage effectively in the running state. Meanwhile, it can also eliminate the interference of functional parts such as lateral connections during the identification process, thereby improving the identification accuracy. The present study has shown both theoretical significance and application value in the field of defect detection and recognition.

Wastewater treatment is a critical and necessary task every human settlement is obligated to address. If not, the consequences might be catastrophic, not just for humans but for the ecosystems as well, pushing research into finding new ways to improve wastewater treatment processes to make them safer and more efficient. Hence, there is a need to address matters, such as reliability and maintainability of Wastewater Treatment Plants (WWTP), when analyzing the availability and operational conditions. These should be addressed by analyzing the plant operational effectiveness impact (P-OEI), and in this article specifically, a WWTP study case to identify design flaws or improvement opportunities. A vital aspect of a complex system is to determine the contribution to resilience, reliability, and availability of every element embedded in the system. This is performed by adapting and applying the P-OEI methodology and real data of a WWTP located in Chile. This methodology breaks down the system into several levels of disaggregation similar to RBD methodology, analyzing the upstream for availability and the downstream for the P-OEI analysis from the system itself to the individual elements within subsystems. The potential impact on the overall system’s lack of efficiency is also quantified by an Expected Operational Impact (EOI) index, which is also calculated by the methodology. The P-OEI and EOI analyses performed in this study are powerful tools to assess the design and performance of complex systems and WWTP in particular.

The antimicrobial activity of garlic (Allium sativum L.) and onion (Allium cepa L.) plant active extracts was determined against Listeria monocytogenes in two meat products. Samples of sausages “cacciatore” and cooked ham in vacuum-packaged slices were artificially contaminated, and the presence of Listeria was evaluated during the sausages ripening and throughout the shelf-life of the cooked ham. The test carried out on sausages did not show differences among treated and untreated samples. The antagonistic activity of the plant extracts against the pathogen was probably hidden by the competition from the sausages microbial flora and the pH and the water activity (aw) decrease. On the other hand, the plant extracts determined an initial reduction of about 1.00 log cfu/g of the L. monocytogenes viable count in the cooked ham slices contaminated with 103 cfu/g, but the best result was obtained with the contamination of 102 cfu/g of L. monocytogenes. In addition to the pathogen’s initial decrease, we observed an extension of the lag phase and a reduction of the Listeria growth rate. Considering that the presence of L. monocytogenes during the slicing phase of the cooked ham does not exceed 10 cfu/g, the use of plant extracts can lead to complete pathogen elimination.