Editor’s Choice Articles

As part of the study project of the pictorial cycle, attributed to Saturnino Gatti, in the church of San Panfilo at Villagrande di Tornimparte (AQ), image analyses were performed in order to document the general conservation conditions of the surfaces, and to map the different painting materials to be subsequently examined using spectroscopic techniques. To acquire the images, radiation sources, ranging from ultraviolet to near infrared, were used; analyses of ultraviolet fluorescence (UVF), infrared reflectography (IRR), infrared false colors (IRFC), and optical microscopy in visible light (OM) were carried out on all the panels of the mural painting of the apsidal conch. The Hypercolorimetric Multispectral Imaging (HMI) technique was also applied in selected areas of two panels. Due to the accurate calibration system, this technique is able to obtain high-precision colorimetric and reflectance measurements, which can be repeated for proper surface monitoring. The integrated analysis of the different wavelengths’ images—in particular, the ones processed in false colors—made it possible to distinguish the portions affected by retouching or repainting and to recover the legibility of some figures that showed chromatic alterations of the original pictorial layers. The IR reflectography, in addition to highlighting the portions that lost materials and were subject to non-original interventions, emphasized the presence of the underdrawing, which was detected using the spolvero technique. UVF photography led to a preliminary mapping of the organic and inorganic materials that exhibited characteristic induced fluorescence, such as a binder in correspondence with the original azurite painting or the wide use of white zinc in the retouched areas. The collected data made it possible to form a better iconographic interpretation. Moreover, it also enabled us to accurately select the areas to be investigated using spectroscopic analyses, both in situ and on micro-samples, in order to deepen our knowledge of the techniques used by the artist to create the original painting, and to detect subsequent interventions. Full article

The chestnut tree (Castanea sativa, Mill.) is a widespread plant in Europe whose fruits and wood has a relevant economic impact. Chestnut wood (CW) is rich in high-value compounds that exhibit various biological activities, such as antioxidant as well as anticarcinogenic and antimicrobial properties. These metabolites can be mainly divided into monomeric polyphenols and tannins. In this piece of work, we investigated a sustainable protocol to isolate enriched fractions of the above-mentioned compounds from CW residues. Specifically, a sequential extraction protocol, using subcritical water, was used as a pre-fractionation step, recovering approximately 88% of tannins and 40% of monomeric polyphenols in the first and second steps, respectively. The optimized protocol was also tested at pre-industrial levels, treating up to 13.5 kg CW and 160 L of solution with encouraging results. Ultra- and nanofiltrations were used to further enrich the recovered fractions, achieving more than 98% of the tannin content in the heavy fraction, whilst the removed permeate achieved up to 752.71 mg GAE/g_ext after the concentration (75.3%). Samples were characterized by means of total phenolic content (TPC), antioxidant activity (DPPH· and ABTS·), and tannin composition (hydrolysable and condensed). In addition, LC-MS-DAD was used for semiqualitative purposes to detect vescalagin/castalagin and vescalin/castalin, as well as gallic acid and ellagic acid. The developed valorization protocol allows the efficient fractionation and recovery of the major polyphenolic components of CW with a sustainable approach that also evaluates pre-industrial scaling-up. Full article

Extreme weather events can trigger various hydrogeomorphic phenomena and processes including slope failures. These shallow instabilities are difficult to monitor and measure due to the spatial and temporal scales in which they occur. New technologies such as unmanned aerial systems (UAS), photogrammetry and the structure-from-motion (SfM) technique have recently demonstrated capabilities useful in performing accurate terrain observations that have the potential to provide insights into these geomorphic processes. This study explores the use of UAS-aided photogrammetry and change detection, using specialized techniques such as the digital elevation model (DEM) of differences (DoD) and cloud-to-cloud distance (C2C) to monitor and quantify geomorphic changes before and after an extreme medicane event in Myrtos, a highly visited touristic site on Cephalonia Island, Greece. The application demonstrates that the combination of UAS with photogrammetry allows accurate delineation of instabilities, volumetric estimates of morphometric changes, insights into erosion and deposition processes and the delineation of higher-risk areas in a rapid, safe and practical way. Overall, the study illustrates that the combination of tools facilitates continuous monitoring and provides key insights into geomorphic processes that are otherwise difficult to observe. Through this deeper understanding, this approach can be a stepping stone to risk management of this type of highly-visited sites, which in turn is a key ingredient to sustainable development in high-risk areas. Full article

In recent years, CO₂ flooding has emerged as an efficient method for improving oil recovery. It also has the advantage of storing CO₂ underground. As one of the promising types of CO₂ enhanced oil recovery (CO₂-EOR), CO₂ water-alternating-gas injection (CO₂-WAG) can suppress CO₂ fingering and early breakthrough problems that occur during oil recovery by CO₂ flooding. However, the evaluation of CO₂-WAG is strongly dependent on the injection parameters, which in turn renders numerical simulations computationally expensive. So, in this work, machine learning is used to help predict how well CO₂-WAG will work when different injection parameters are used. A total of 216 models were built by using CMG numerical simulation software to represent CO₂-WAG development scenarios of various injection parameters where 70% of them were used as training sets and 30% as testing sets. A random forest regression algorithm was used to predict CO₂-WAG performance in terms of oil production, CO₂ storage amount, and CO₂ storage efficiency. The CO₂-WAG period, CO₂ injection rate, and water–gas ratio were chosen as the three main characteristics of injection parameters. The prediction results showed that the predicted value of the test set was very close to the true value. The average absolute prediction deviations of cumulative oil production, CO₂ storage amount, and CO₂ storage efficiency were 1.10%, 3.04%, and 2.24%, respectively. Furthermore, it only takes about 10 s to predict the results of all 216 scenarios by using machine learning methods, while the CMG simulation method spends about 108 min. It demonstrated that the proposed machine-learning method can rapidly predict CO₂-WAG performance with high accuracy and high computational efficiency under conditions of various injection parameters. This work gives more insights into the optimization of the injection parameters for CO₂-EOR. Full article

Air overpressure (AOp) is an undesirable environmental effect of blasting. To date, a variety of empirical equations have been developed to forecast this phenomenon and prevent its negative impacts with accuracy. However, the accuracy of these methods is not sufficient. In addition, they are resource-consuming. This study employed support vector regression (SVR) optimized with the grasshopper optimizer (GO) algorithm to forecast AOp resulting from blasting. Additionally, a novel input selection technique, the Boruta algorithm (BFS), was applied. A new algorithm, the SVR-GA-BFS₇, was developed by combining the models mentioned above. The findings showed that the SVR-GO-BFS₇ model was the best technique (R² = 0.983, RMSE = 1.332). The superiority of this model means that using the seven most important inputs was enough to forecast the AOp in the present investigation. Furthermore, the performance of SVR-GO-BFS₇ was compared with various machine learning techniques, and the model outperformed the base models. The GO was compared with some other optimization techniques, and the superiority of this algorithm over the others was confirmed. Therefore, the suggested method presents a framework for accurate AOp prediction that supports the resource-saving forecasting methods. Full article

The 27 September 2021 damaging mainshock (Mw6.0) close to Arkalochori village is the strongest earthquake that was recorded during the instrumental period of seismicity in Central Crete (Greece). The mainshock was preceded by a significant number of foreshocks that lasted nearly four months. Maximum ground subsidence of about 18 cm was estimated from InSAR processing. The aftershock sequence is located in an almost NE-SW direction and divided into two main clusters, the southern and the northern ones. The foreshock activity, the deformation area, and the strongest aftershocks are located within the southern cluster. Based on body-wave travel times, a 3-D velocity model was developed, while using combined space and ground-based geodetic techniques, the co-seismic ground deformation is presented. Moreover, we examined the co-seismic static stress changes with respect to the aftershocks’ spatial distribution during the major events of the foreshocks, the Mw = 6.0 main event as well as the largest aftershock. Both the foreshock and the aftershock sequences obey the scaling law for the frequency-magnitude distribution as derived from the framework of non-extensive statistical physics (NESP). The aftershock production rate decays according to the modified Omori scaling law, exhibiting various Omori regimes due to the generation of secondary aftershock sequences. The analysis of the inter-event time distribution, based on NESP, further indicates asymptotic power-law scaling and long-range correlations among the events. The spatiotemporal evolution of the aftershock sequence indicates triggering by co-seismic stress transfer, while its slow migration towards the outer edges of the area of the aftershocks, related to the logarithm of time, further indicates a possible afterslip. Full article

Background: This study targets the development of an explainable deep learning methodology for the automatic classification of coronary artery disease, utilizing SPECT MPI images. Deep learning is currently judged as non-transparent due to the model’s complex non-linear structure, and thus, it is considered a «black box», making it hard to gain a comprehensive understanding of its internal processes and explain its behavior. Existing explainable artificial intelligence tools can provide insights into the internal functionality of deep learning and especially of convolutional neural networks, allowing transparency and interpretation. Methods: This study seeks to address the identification of patients’ CAD status (infarction, ischemia or normal) by developing an explainable deep learning pipeline in the form of a handcrafted convolutional neural network. The proposed RGB-CNN model utilizes various pre- and post-processing tools and deploys a state-of-the-art explainability tool to produce more interpretable predictions in decision making. The dataset includes cases from 625 patients as stress and rest representations, comprising 127 infarction, 241 ischemic, and 257 normal cases previously classified by a doctor. The imaging dataset was split into 20% for testing and 80% for training, of which 15% was further used for validation purposes. Data augmentation was employed to increase generalization. The efficacy of the well-known Grad-CAM-based color visualization approach was also evaluated in this research to provide predictions with interpretability in the detection of infarction and ischemia in SPECT MPI images, counterbalancing any lack of rationale in the results extracted by the CNNs. Results: The proposed model achieved 93.3% accuracy and 94.58% AUC, demonstrating efficient performance and stability. Grad-CAM has shown to be a valuable tool for explaining CNN-based judgments in SPECT MPI images, allowing nuclear physicians to make fast and confident judgments by using the visual explanations offered. Conclusions: Prediction results indicate a robust and efficient model based on the deep learning methodology which is proposed for CAD diagnosis in nuclear medicine. Full article

Valorization of agri-food residues to produce bio-based platform chemicals will enhance the transition to the bio-economy era. To this end, a sustainable process has been developed for the overall valorization of grape stalks (GS) according to a circular approach, starting from the lignin fraction to further deal with the cellulose-rich residue. This non-conventional protocol fully adheres to green chemistry principles, exploiting the so-called enabling technologies—mainly ultrasound and microwaves—for energy-saving innovative processes. Firstly, ultrasound-assisted extraction (UAE, 40 kHz, 200 W) demonstrated to be an excellent technique for GS delignification combined with natural deep eutectic solvents (NaDESs). Delignification enables isolation of the pertinent lignin framework and the potential to obtain a polyphenol-rich liquid fraction, focusing on the valorization of GS as source of bioactive compounds (BACs). Among the NaDESs employed, the combination of choline chloride (ChCl) and levulinic acid (LevA) (ChLevA) presented noteworthy results, enabling a delignification higher than 70%. LevA is one of the top-value biobased platform chemicals. In this work, a flash microwave (MW)-assisted process was subsequently applied to the cellulose-rich fraction remained after delignification, yielding 85% LevA. The regeneration of this starting compound to produce ChLevA can lead to a further biomass delignification cycle, thus developing a new cascade protocol for a full valorization of GS. Full article

The data collection in the automated monitoring of landslides is often characterized by large amounts of data, periodic fluctuations, many outliers, and different collection intervals. The traditional method of calculating velocity and acceleration using the differential algorithm for landslide acceleration relies on experience to select thresholds and produces a large number of false early warnings. A hybrid early warning method for the landslide acceleration process based on automated monitoring data is proposed to solve this problem. The method combines the conventional warning method, based on cumulative displacement, velocity, and acceleration, and the critical sliding warning method based on normalized tangent angle according to different strategies. On the one hand, the least-squares fitting of monitoring data inside a given time window is used to calculate various early warning parameters, improving data usage and lowering calculation error. On the other hand, a dynamic semi-quantitative and semi-empirical method is provided for the determination of the thresholds, which is more reliable than the purely empirical method. The validation experiments at the Lishanyuan landslide in southern China show that the hybrid method can accurately identify the accelerating deformation of the landslide and gives very few false warnings. The proposed method is practical and effective for systems that require automated monitoring and warnings for a large number of landslides. Full article

Photonic transmitters that operate with a high data transfer rate (over 150 Gb/s) at the O-band range (1260–1360 nm) require at least 100 milliwatts of power to overcome the power losses that are caused by using high-speed modulators. A laser with higher power can probably handle this requirement; however, for the transmitter system, this solution can be problematic due to the nonlinear effects that can happen, which may lead to high noise in the transmitter system. Thus, to solve this issue, we propose a new design of a 2 × 1 multimode interference (MMI) power combiner using silicon nitride (SiN) slot waveguide structures. The MMI power combiner and the SiN slot waveguide structures were optimized using the full-vectorial beam propagation method (FV-BPM) and the finite difference time domain (FDTD) method. After combining two sources, high efficiency was obtained of 94.8–97.6% from the total power after a short coupling length of 109.81 µm over the O-band range with a low back reflection of 44.94 dB. Thus, the proposed device can be very useful for combining two O-band sources to gain a higher power level, which can be utilized to improve performances in transmitter systems. Full article

The main source of the ionization of the Earth’s atmosphere is the cosmic radiation that depends on solar activity as well as geomagnetic activity. Galactic cosmic rays constitute a permanent radiation background and contribute significantly to the radiation exposure inside the atmosphere. In this work, the cosmic-ray-induced ionization of the Earth’s atmosphere, due to both solar and galactic cosmic radiation during the recent solar cycles 23 (1996–2008) and 24 (2008–2019), was studied globally. Estimations of the ionization were based on the CRAC:CRII model by the University of Oulu. The use of this model allowed for extensive calculations from the Earth’s surface (atmospheric depth 1033 g/cm²) to the upper limit of the atmosphere (atmospheric depth 0 g/cm²). Monte Carlo simulations were performed for the estimation quantities of radiobiological interest with the validated software DYASTIMA/DYASTIMA-R. This study was focused on specific altitudes of interest, such as the common flight levels used by commercial aviation. Full article

The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability prior to the construction procedure. In this study, the application of Tree-Based machine learning techniques is implemented to analyze the ultimate strain of non-spliced and spliced steel reinforcements. In this regard, a database containing the results of 225 experimental tests was collected based on the research investigations available in peer-reviewed international publications. The database included the mechanical properties of both non-spliced and mechanically spliced bars. For better accuracy, the databases of other splicing methods such as lap and welded-spliced methods were excluded from this research. The database was categorized as two sub-databases: training (85%) and testing (15%) of the developed models. Various effective parameters such as splice technique, steel grade of the bar, diameter of the steel bar, coupler geometry—including length and outer diameter along with the testing temperatures—were defined as the input variables for analyzing the ultimate strain using tree-based approaches including Decision Trees and Random Forest. The predicted outcomes were compared to the actual values and the precision of the prediction models was assessed via performance metrics, along with a Taylor diagram. Based on the reported results, the reliability of the proposed ML-based methods was acceptable (with an R² ≥ 85%) and they were time-saving and cost-effective compared to more complicated, time-consuming, and expensive experimental examinations. More importantly, the models proposed in this study can be further considered as a part of a comprehensive prediction model for estimating the stress-strain behavior of steel bars. Full article

The Architecture, Engineering, and Construction (AEC) industries are the producers of the most significant waste stream in the European Union. Known EU initiatives propose to deal with the issue of construction and demolition waste (CDW) according to the principles of a circular economy: the 3Rs (reduce, reuse, and recycle). CDW is generated during the whole life cycle of construction. The lack of information about the quantity of CDW during the design phase of building needed for sustainable design of construction was identified as a research gap. The aim of our research is to quantify construction and demolition waste during the construction design phase in a circular economy. The proposed method is based on the generation rate calculation method. This paper describes the proposed methodology for quantifying selected types of construction waste: excavated soil, concrete, and masonry. This information is essential from the point of view of a sustainable circular economy. The main contributions of the paper were identified during the decision-making process of sustainable building design, during the audit of CDW management, and during building information modelling as a support tool for CDW management. As early as the construction design phase, there is the possibility of choosing technologies, construction processes, and materials that have a higher degree of circularity in the economy. Full article

In this study, four protective coating materials: Inconel 718, Inconel 625, Alloy 33 and Stellite 6 were deposited on 16Mo3 steel tubes by means of CMT (Cold Metal Transfer), as an advanced version of MAG (Metal Active Gas) welding method. In the next step, the surface of the deposited coating was remelted by means of TIG (Tungsten Inert Gas) welding method. SEM microstructure of coatings–substrate has been reported, and an EDX-researched chemical composition of the coatings was compared to the nominal chemical composition. The hardness distribution in the cross-section was performed, which revealed that among investigated coatings, Stellite 6 layer is the hardest, at about 500 HV0.2. Other materials such as Inconel 625, Inconel 718 and Alloy 33 represented a cladded zone hardness about 250 HV0.2. Stellite 6 layer had the lowest wear resistance in the dry sand/rubber wheel test, and Stellite 6 layer had the highest wear resistance in the erosive blasting test. This proved the existence of different wear mechanisms in the two test methods used. In the dry sand/rubber wheel test, the Alloy 33 and Inconel 718 only represented higher wear resistance than substrate 16Mo3 steel. In abrasive blasting tests all coatings had higher wear resistance than 16Mo3 steel; however, Stellite 6 coatings represented an approximately 5 times higher durability than other investigated (Inconel 625, Inconel 718, and Alloy 33) coatings. Full article

Quality control of car body paint in the automotive industry is a promising industrial application of terahertz technology. Terahertz time-domain spectroscopy in reflection geometry enables accurate, fast, and nondestructive measurement of individual layer thicknesses of multi-layer coatings. For high precision thickness measurements, the frequency dependent complex refractive index of all layers must be calibrated very accurately. THz time-domain ellipsometry is self-referencing and provides reliable, frequency resolved material properties with high signal-to-noise ratio. The method is characterized by a high sensitivity to optical material properties and layer thicknesses. We present characterization results in the frequency range 0.1–6 THz for typical automotive paints and different substrates such as polypropylene (PP), which features a high material anisotropy. We demonstrate that the broadband material properties derived from ellipsometry allow for inline thickness measurements of multi-layer car body paints with high accuracy. Full article

We investigate fabrication of compacts using polytetrafluoroethylene (PTFE) and polyethylene (PE), and the effect of compaction conditions on their terahertz transmission properties. The conditions used to fabricate compressed powder samples for terahertz time-domain spectroscopy (THz-TDS) can impact the accuracy of the measurements and hence the interpretation of results. This study investigated the effect of compaction conditions on the accuracy of the THz-TDS analysis. Two polymers that are commonly used as matrix materials in terahertz spectroscopy studies were explored using a compaction simulator and a hydraulic press for sample preparation. THz-TDS was used to determine the refractive index and loss coefficient to compare the powder compacts (pellets) to the values of solid material. Sample porosity, axial relaxation and tensile strength were measured to assess the material’s suitability for terahertz spectroscopy. It was found that PTFE is the preferable material for creating THz-TDS samples due to its low porosity and high tensile strength. PE was found to show significant porosity at all compaction pressures, making it an unsuitable material for the accurate determination of optical parameters from THz-TDS spectroscopy measurements. The larger particle sizes of PE resulted in compacts that exhibited significantly lower tensile strength than those made from PTFE making handling and storage difficult. Full article

Fire risk will increase in the upcoming years due to climate change. In this context, GIS analysis for fire risk mapping is an important tool to identify high risk areas and allocate resources. In the present study, we aimed to create a fire risk estimation model that incorporates recent land cover changes, along with other important risk factors. As a study area, we selected Dadia-Lefkimi-Soufli National Forest Park and the surrounding area since it is one of the most important protected areas in Greece. The area selected for the case study is a typical Mediterranean landscape. As a result, the outcome model is generic and can be applied to other areas. In order to incorporate land cover changes in our model, we used a support vector machine (SVM) algorithm to classify a satellite image captured in September 2021 and an image of the same period two years ago to obtain comparable results. Next, two fire risk maps were created with a combination of land cover and six other factors, using the analytic hierarchy process (AHP) on a GIS platform. The results of our model revealed noticeable clusters of extreme high risk areas, while the overall fire risk in the National Park Forest of Dadia-Lefkimi-Soufli was classified as high. The wildfires of 1st October 2020 and 9th July 2021 confirmed our model and contributed to quantification of their impact on fire risk due to land cover change. Full article

The Arkalochori village in central Crete was hit by a large earthquake (M_w = 6.0) on 27 September 2021, causing casualties, injuries, and severe damage to the infrastructure. Due to the absence of apparent surface rupture and the initial focal mechanism solution of the seismic event, we initiated complementary, multi-disciplinary research by combining seismological and remote sensing data processing, followed by extensive field validation. Detailed geological mapping, fault surface measuring accompanied with tectonic analysis, fault photorealistic model creation by unmanned aerial system data processing, post-seismic surface deformation analysis by DInSAR image interpretation coupled with accurately relocated epicenters recorded by locally established seismographs have been carried out. The combination of the results obtained from these techniques led to the determination of the contemporary tectonic stress regime that caused the earthquake in central Crete, which was found compatible with extensional processes parallel to the Hellenic arc. Full article

Several stress-strain models were used to predict the strengths of steel fiber reinforced concrete, which are distinctive of the material. However, insufficient research has been done on the influence of hybrid fiber combinations (comprising two or more distinct fibers) on the characteristics of concrete. For this reason, the researchers conducted an experimental program to determine the stress-strain relationship of 30 concrete samples reinforced with two distinct fibers (a hybrid of polyvinyl alcohol and steel fibers), with compressive strengths ranging from 40 to 120 MPa. A total of 80% of the experimental results were used to develop a new empirical stress-strain model, which was accomplished through the application of the particle swarm optimization (PSO) technique. It was discovered in this investigation that the new stress-strain model predictions are consistent with the remaining 20% of the experimental stress-strain curves obtained. Case studies of hybrid–fiber–reinforced concrete constructions were investigated in order to better understand the behavior of such elements. The data revealed that the proposed model has the highest absolute relative error (ARE) frequencies (ARE 10%) and the lowest absolute relative error (ARE > 15%) frequencies (ARE > 15%). Full article

The trend of data center transceivers is to increase bandwidth while simultaneously decreasing their size, power consumption, and cost. While data center links have previously relied on vertical-cavity surface-emitting lasers (VCSELs) or in-plane lasers using coarse wavelength division multiplexing (WDM) to encode data, recently, dense WDM (DWDM) has moved to the forefront for next-generation links. Several approaches exist as light sources for DWDM links; DFB arrays, nonlinear microcombs, and semiconductor mode-locked lasers (MLLs). This paper focuses on quantum dot MLLs (QDMLLs), which currently offer the best efficiency, simplicity, and size. The efficiency of optical combs generated by QDMLLs is analyzed in depth in this study. Full article

First-principles electronic band calculations were used to investigate the effects of alkali metals and organic cations added to Cu-based perovskite solar cells. The copper d-orbital band was slightly above the valence-band maximum and functioned as an acceptor level for carrier generation. Excitation from iodine p-orbitals and copper d-orbitals to alkali metal s-orbitals could suppress carrier recombination and promote carrier transport. Experimental solar conversion efficiencies increased after adding both Cu and Na, in agreement with the calculations. Total-energy calculations indicated that the perovskite crystal stability increased with the addition of ethyl ammonium, although the total energy decreased with the addition of Cu and Na. Full article

Train speed is increasing due to the development of high-speed railway technology. However, high-speed trains generate more noise and discomfort for residents, enclosed housing for sound emission alleviation is needed to further reduce noise. Because these enclosed housings for sound emission alleviation restrain the air flow, strong and complicated aerodynamic pressures are generated inside the housing for sound emission alleviation when a train passes through at a high speed. This train-induced aerodynamic pressure, particularly its dynamic characteristics, is a key parameter in structural design. In the present study, the train-induced unsteady aerodynamic pressure in an enclosed housing for sound emission alleviation is simulated using the dynamic mesh method, and the dynamic characteristics of the aerodynamic pressure are investigated. The simulation results show that when the train is running in the enclosed housing for sound emission alleviation, the unsteady aerodynamic pressure is complicated and aperiodic, and after the train leaves the housing for sound emission alleviation, the aerodynamic pressure reverts to periodic decay curves. Two new terms, the duration of the extreme aerodynamic pressure and the pressure change rate, are proposed to evaluate the dynamic characteristics when the train passes through the barrier. The dominant frequency and decay rate are adopted to express the dynamic characteristics after the train exits. When the train runs in the enclosed housing for sound emission alleviation, the longest durations of the positive and negative extreme aerodynamic pressures are in the middle section, and the maximum change rate of aerodynamic pressure occurs at the entrance area. After the train exits the housing for sound emission alleviation, the pressure amplitude at the central region is always higher than those close to the entrance/exit. The dominant frequency of the aerodynamic pressure is identified and explained using wave propagation theory, the decay rate of the aerodynamic pressure at all sections is close. Full article

The integration of sustainability in the circular economy is an emerging paradigm that can offer a long term vision to achieve environmental and social sustainability targets in line with the United Nation’s Sustainable Development Goals. Developing scalable and sustainable impacts in circular economy business models (CEBMs) has many challenges. While many advanced technology manufacturing firms start as small enterprises, remarkably little is known about how material reuse firms in sociotechnical systems transition towards circular business models. Research into CEBMs integrating sustainability and environmental conservation is still in its early stages. There has been increased interest in sustainability and circular economy research, but current research is fragmented. The innovation surrounding CEBMs eludes some firms with relatively limited evidence of the transitional perspective necessary to integrate aspects of sustainability. This lack of evidence is especially applicable to the context of circular economy practices in small and medium enterprises in the United States regarding capabilities, operations obstacles, and elements of success in designing circular business models. Based on a qualitative, interview-based inductive study of a material reuse firm, our research develops a conceptual model of the critical success factors and obstacles that are part of implementing circular economy practices. Firms must first manage strategic enablers and monitor tactical enablers to achieve sustainability goals. In this study, we identify the underlying enablers of how these capabilities affect the transition to a CEBM that integrates sustainability. The framework emerging from our findings highlights the interplay of CEBM, innovation success factors, and obstacles at a micro-level. The investigation of a material reuse firm serves as the foundation for developing a framework for how managers can alter a company and revise the business model to transition towards a more innovative circular economy. Full article

Nuclear reactions between protons and boron-11 nuclei (p–B fusion) that were used to yield energetic α-particles were initiated in a plasma that was generated by the interaction between a PW-class laser operating at relativistic intensities (~3 × 10¹⁹ W/cm²) and a 0.2-mm thick boron nitride (BN) target. A high p–B fusion reaction rate and hence, a large α-particle flux was generated and measured, thanks to a proton stream accelerated at the target’s front surface. This was the first proof of principle experiment to demonstrate the efficient generation of α-particles (~10¹⁰/sr) through p–B fusion reactions using a PW-class laser in the “in-target” geometry. Full article

The agricultural sector has benefitted over the last century from several factors that have led to an exponential increase in its productive efficiency. The increasing use of new materials, such as plastics, has been one of the most important factors, as they have allowed for increased production in a simpler and more economical way. Various polymer types are used in different phases of the agricultural production cycle, but when their use is incorrectly managed, it can lead to different environmental impacts. In this study, an applied and simplified methodology to manage agricultural plastics monitoring and planning is proposed. The techniques used are based on quantification through the use of different datasets (orthophotos and satellite images) of the areas covered by plastics used for crop protection. The study area chosen is a part of the Ionian Coast of Southern Italy, which includes the most important municipalities of the Basilicata Region for fruit and vegetable production. The use of geographical techniques and observation methodologies, developed in an open-source GIS environment, enabled accurate location of about 2000 hectares of agricultural land covered by plastics, as well as identification of the areas most susceptible to the accumulation of plastic waste. The techniques and the model implemented, due to its simplicity of use and reliability, can be applied by different local authorities in order to realize an Atlas of agricultural plastics, which would be applied for continuous monitoring, thereby enabling the upscaling of future social and ecological impact assessments, identification of new policy impacts, market searches, etc. Full article

This paper introduces a climatic multi-hazard risk assessment for Greece, as the first-ever attempt to enhance scientific knowledge for the identification and definition of hazards, a critical element of risk-informed decision making. Building on an extensively validated climate database with a very high spatial resolution (5 × 5 km²), a detailed assessment of key climatic hazards is performed that allows for: (a) the analysis of hazard dynamics and their evolution due to climate change and (b) direct comparisons and spatial prioritization across Greece. The high geographical complexity of Greece requires that a large number of diverse hazards (heatwaves—TX, cold spells—TN, torrential rainfall—RR, snowstorms, and windstorms), need to be considered in order to correctly capture the country’s susceptibility to climate extremes. The current key findings include the dominance of cold-temperature extremes in mountainous regions and warm extremes over the coasts and plains. Extreme rainfall has been observed in the eastern mainland coasts and windstorms over Crete and the Aegean and Ionian Seas. Projections of the near future reveal more warm extremes in northern areas becoming more dominant all over the country by the end of the century. Full article

Unitary decomposition is a widely used method to map quantum algorithms to an arbitrary set of quantum gates. Efficient implementation of this decomposition allows for the translation of bigger unitary gates into elementary quantum operations, which is key to executing these algorithms on existing quantum computers. The decomposition can be used as an aggressive optimization method for the whole circuit, as well as to test part of an algorithm on a quantum accelerator. For the selection and implementation of the decomposition algorithm, perfect qubits are assumed. We base our decomposition technique on Quantum Shannon Decomposition, which generates $O\left(\frac{3}{4}{4}^n\right)$ controlled-not gates for an n-qubit input gate. In addition, we implement optimizations to take advantage of the potential underlying structure in the input or intermediate matrices, as well as to minimize the execution time of the decomposition. Comparing our implementation to Qubiter and the UniversalQCompiler (UQC), we show that our implementation generates circuits that are much shorter than those of Qubiter and not much longer than the UQC. At the same time, it is also up to 10 times as fast as Qubiter and about 500 times as fast as the UQC. Full article

Pipes of various shapes constitute pipelines utilized in industrial sites. These pipes are coupled through welding, wherein complex curvatures such as a flange, an elbow, a reducer, and a branch pipe are often found. Using phased array ultrasonic testing (PAUT) to inspect weld zones with complex curvatures is faced with different challenges due to parts that are difficult to contact with probes, small-diameter pipes, spatial limitations due to adjacent pipes, nozzles, and sloped shapes. In this study, we developed a flexible PAUT probe (FPAPr) and a semi-automatic scanner that was improved to enable stable FPAPr scanning for securing its inspection data consistency and reproducibility. A mock-up test specimen was created for a flange, an elbow, a reducer, and a branch pipe. Artificial flaws were inserted into the specimen through notch and hole processing, and simulations and verification experiments were performed to verify the performance and field applicability of the FPAPr and semi-automatic scanner. Full article

Application desired functionality as well as operation expenses of cold atmospheric pressure plasma (CAP) devices scale with properties like gas selection. The present contribution provides a comparative investigation for a CAP system operated in argon or helium at different operation voltages and distance to the surface. Comparison of power dissipation, electrical field strength and optical emission spectroscopy from vacuum ultraviolet over visible up to near infrared ((V)UV-VIS-NIR) spectral range is carried out. This study is extended to safety relevant investigation of patient leakage current, induced surface temperature and species density for ozone (O${}_3$) and nitrogen oxides (NO${}_x$). It is found that in identical operation conditions (applied voltage, distance to surface and gas flow rate) the dissipated plasma power is about equal (up to 10 W), but the electrical field strength differs, having peak values of 320 kV/m for Ar and up to 300 kV/m for He. However, only for Ar CAP could we measure O${}_3$ up to 2 ppm and NO${}_x$ up to 7 ppm. The surface temperature and leakage values of both systems showed different slopes, with the biggest surprise being a constant leakage current over distance for argon. These findings may open a new direction in the plasma source development for Plasma Medicine. Full article

Soils contain an increasing number of different pollutants, which are often released into the environment by human activity. Among the “new” potential pollutants are plastics and microplastics. “Recognized” pollutants such as heavy metals, of geogenic and anthropogenic origin, now meet purely anthropogenic contaminants such as plastic particles. Those can meet especially in floodplain landscapes and floodplain soils, because of their function as a temporary sink for sediments, nutrients, and pollutants. Based on a geospatial sampling approach, we analyzed the soil properties and heavy metal contents (ICP-MS) in soil material and macroplastic particles, and calculated total plastic concentrations (Ptot) from preliminary studies. Those data were used to investigate spatial connections between both groups of pollutants. Our results from the example of the Lahn river catchment show a low-to-moderate contamination of the floodplain soils with heavy metals and a wide distribution of plastic contents up to a depth of two meters. Furthermore, we were able to document heavy metal contents in macroplastic particles. Spatial and statistical correlations between both pollutants were found. Those correlations are mainly expressed by a comparable variability in concentrations across the catchment and in a common accumulation in topsoil and upper soil or sediment layers (0–50 cm). The results indicate comparable deposition conditions of both pollutants in the floodplain system. Full article

Communities are confronted with the rapidly growing impact of disasters, due to many factors that cause an increase in the vulnerability of society combined with an increase in hazardous events such as earthquakes and floods. The possible impacts of such events are large, also in developed countries, and governments and stakeholders must adopt risk reduction strategies at different levels of management stages of the communities. This study is aimed at proposing a sound qualitative multi-hazard risk analysis methodology for the assessment of combined seismic and hydraulic risk at the regional scale, which can assist governments and stakeholders in decision making and prioritization of interventions. The method is based on the use of machine learning techniques to aggregate large datasets made of many variables different in nature each of which carries information related to specific risk components and clusterize observations. The framework is applied to the case study of the Emilia Romagna region, for which the different municipalities are grouped into four homogeneous clusters ranked in terms of relative levels of combined risk. The proposed approach proves to be robust and delivers a very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling at the regional scale. Full article

It is important and difficult to improve the tensile strength of backfill material to ensure the stability of goafs. In this study, rice straw (RS) in fiber form is used to improve the tensile properties of cemented paste backfill (CPB). An orthogonal experiment was designed, Brazilian indirect tensile strength tests were conducted to test the tensile performance of RS fiber-reinforced cemented paste backfill (RSCPB) under different fiber content (1, 2, 3 kg/m³) and fiber length (0.8~1, 1~3, 3~5 cm), and the microstructure of RSCPB was analyzed with scanning electron microscopy (SEM). The results showed that, compared with the conventional cemented paste backfill (CCPB), the increase in tensile strength of RSCPB ranged from 115.38% to 300.00% at 3 days curing age, 40.91% to 346.15% at 7 days, and −38.10% to 28.00% at 28 days, and the strain was slightly reduced during the curing period. The tensile strength, strain, and percentage increase of the RSCPB compared to the CCBP did not show a monotonic pattern of variation with the RS fiber content and length during the curing period. The RSCPB samples fractured under peak stress, showing obvious brittle failure. In addition, sulfate generated from S²⁻ in the tailings inhibits the hydration reaction, and generates swelling products that form weak structural surfaces, which, in turn, lead to a 28-day tensile strength and strain of RSCPB lower than those at 7 days. Full article

The Water-Cooled Lead–Lithium Breeding Blanket (WCLL BB) is one of the two blanket concept candidates to become the driver blanket of the EU-DEMO reactor. The design was enacted with a holistic approach. The influence that neutronics, thermal-hydraulics (TH), thermo-mechanics (TM) and magneto-hydro-dynamics (MHD) may have on the design were considered at the same time. This new approach allowed for the design team to create a WCLL BB layout that is able to comply with different foreseen requirements in terms of integration, tritium self-sufficiency, and TH and TM needs. In this paper, the rationale behind the design choices and the main characteristics of the WCLL BB needed for the EU-DEMO are reported and discussed. Finally, the main achievements reached during the pre-conceptual design phase and some remaining open issues to be further investigated in the upcoming conceptual design phase are reported as well. Full article

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. Full article

Xenobiotic Triclosan (TCS) is of great concern because of its existence in a variety of personal, household and healthcare products and continuous discharge in water worldwide. Excessive use of TCS-containing sanitizers and antiseptic products during the COVID-19 pandemic further increased its content in aquatic ecosystems. The present study deals with the cyto-genotoxic effects and biochemical alterations in the hatchlings of Labeo rohita on exposure to environmentally relevant concentrations of TCS. Three-days-old hatchlings were exposed to tap water, acetone (solvent control) and 4 environmentally relevant concentrations (6.3, 12.6, 25.2 and 60 µg/L) of TCS for 14 days and kept for a recovery period of 10 days. The significant concentration-dependent decline in cell viability but increase in micronucleated cells, nucleo-cellular abnormalities (NCAs) and DNA damage parameters like tail length, tail moment, olive tail moment and percent of tail DNA after exposure persisted till the end of recovery period. Glucose, triglycerides, cholesterol, total protein, albumin, total bilirubin, uric acid and urea (except for an increase at 60 µg/L) showed significant (p ≤ 0.05) concentration-dependent decrease after 14 days of exposure. The same trend (except for triglycerides, albumin and total bilirubin) continued till 10 days post exposure. In comparison to control, transaminases (alanine and aspartate aminotransferases) increased (p ≤ 0.05) after exposure as well as the recovery period, while a decline in alkaline phosphatase after exposure was followed by a significant increase during the recovery period. The results show that the environmentally relevant concentrations of TCS cause deleterious effects on the hatchlings of L. rohita. Full article

The information in the literature on the effect of printing layer thickness on interim 3D-printed crowns is limited. In the present study, the effect of layer thickness on the trueness and margin quality of 3D-printed composite resin crowns was investigated and compared with milled crowns. The crowns were printed in 3 different layer thicknesses (20, 50, and 100 μm) by using a hybrid resin based on acrylic esters with inorganic microfillers or milled from polymethylmethacrylate (PMMA) discs and digitized with an intraoral scanner (test scans). The compare tool of the 3D analysis software was used to superimpose the test scans and the computer-aided design file by using the manual alignment tool and to virtually separate the surfaces. Deviations at different surfaces on crowns were calculated by using root mean square (RMS). Margin quality of crowns was examined under a stereomicroscope and graded. The data were evaluated with one-way ANOVA and Tukey HSD tests. The layer thickness affected the trueness and margin quality of 3D-printed interim crowns. Milled crowns had higher trueness on intaglio and intaglio occlusal surfaces than 100 μm-layer thickness crowns. Milled crowns had the highest margin quality, while 20 μm and 100 μm layer thickness printed crowns had the lowest. The quality varied depending on the location of the margin. Full article

Peri-implant mucositis represents an inflammatory lesion of the mucosa surrounding an endosseous implant, without the loss of the supporting peri-implant bone. Considering its reversible nature, every effort should be made to contrast it, thus avoiding the eventual progression towards peri-implantitis. The aim of the present randomized clinical trial is to evaluate the efficacy of the ozonized water against peri-implant mucositis. A total of 26 patients diagnosed for this latter clinical condition were randomly divided according to the professional oral hygiene protocol performed on the pathological sites at baseline, at T1 (1 month), and T2 (2 months). Group 1 underwent an ozonized water administration (experimental treatment), whereas Group 2 underwent a pure water one (control treatment). Both administrations were performed with the same professional irrigator (Aquolab^® professional water jet, Aquolab s.r.l. EB2C S.r.l., Milano, Italy) with no differences in color or taste between the two substances delivered. At each appointment, the following indexes were assessed: the Probing Pocket Depth (PPD), Plaque Index (PI), Bleeding on Probing (BoP), and Bleeding Score (BS). As regards intragroup differences, in Group 1 ozonized water significantly and progressively reduced all the clinical indexes tested, except for PI in the period T1–T2, whereas no significant differences occurred within the control group. Despite this, no significant intergroup differences were generally detected between the two treatments. Accordingly, the role of ozone for the management of peri-implant mucositis deserves to be further investigated. Full article

In the recent past, sustainable development has been considered a major issue for urban and regional studies. Adaptive reuse appears to be a practical solution for sustainable urban development. Beyond and in addition to a conceptual base consistent with circular economy and sustainability principles, how do we know if adaptive reuse is actually sustainable, provided that it constitutes a multidisciplinary and multilevel process? The present study aims at evaluating, in as much as feasible quantitative terms, adaptive reuse practices sustainability. This was attained using a set of indicators, developed combining PESTLE (the Political, Economic, Technical, Social, Legal, and Environmental aspects) and SWOT (the Strengths, Weaknesses, Opportunities, and Threats) approaches, of which the results were subjected to evaluation by experts (pairwise comparisons), following the Analytic Hierarchy Process (AHP). The indicators representing strengths and opportunities of the process were calculated to be of higher value (overall level of final cumulative indicators values; 70.4%) compared with indicators representing weaknesses and threats. Enhancing strengths and opportunities and counteracting weaknesses and threats contribute making the potential of adaptive reuse practices in urban sustainability more evident. Among analysis dimensions, political and economic aspects rank first, followed by environmental, socio-cultural, technological-technical, and legal aspect. The empirical results of this paper serve as a useful reference point for decision-making and policy formulation addressing adaptive reuse practices in sustainable development strategies. Full article

Simple bridge displacement measurement using a video camera is effective in realizing the efficient management of numerous railway structures via condition-based maintenance. Although non-marker image measurement is significantly influenced by the measuring environment, its practical applicability considering the displacement measurement accuracy of non-marker images and the influence of various environments is not completely understood. In this study, the accuracy of non-marker image displacement measurement and the influence of illuminance are confirmed using a model bridge, and the accuracy and applicable range are discussed. Moreover, field tests on two bridges—a steel and a concrete bridge—on low-speed and high-speed railways confirm the accuracy and practical application of non-marker image measurement in a real environment. The displacement was observed to be measured with an accuracy of ~1/30 pixel (error of ~0.4 mm at 20 m position) in the daytime with sufficient brightness. Moreover, the settings for subset positions and post-processing methods to ensure accuracy in non-marker image measurement on concrete bridges with low surface contrast are discussed. Full article

In this work, a preliminary numerical assessment on the application of an additive manufactured hybrid metal/composite shock absorber panels to a military seat ejection system, has been carried out. The innovative character of the shock absorber concept investigated is that the absorbing system has a thickness of only 6 mm and is composed of a pyramid-shaped lattice core that, due to its small size, can only be achieved by additive manufacturing. The mechanical behaviour of these shock absorber panels has been examined by measuring their ability to absorb and dissipate the energy generated during the ejection phase into plastic deformations, thus reducing the loads acting on pilots. In this paper the effectiveness of a system composed of five hybrid shock absorbers, with very thin thickness in order to be easily integrated between the seat and the aircraft floor, has been numerically studied by assessing their ability to absorb the energy generated during the primary ejection phase. To accomplish this, a numerical simulation of the explosion has been performed and the energy absorbed by the shock-absorbing mechanism has been assessed. The performed analysis demonstrated that the panels can absorb more than 60% of the energy generated during the explosion event while increasing the total mass of the pilot-seat system by just 0.8%. Full article

Transition metal complexes are attracting attention because of their various chemical and biological properties. In particular, the NHC-gold complexes represent a productive field of research in medicinal chemistry, mostly as anticancer tools, displaying a broad range of targets. In addition to the already known biological targets, recently, an important activity in the organization of the cell cytoskeleton was discovered. In this paper, we demonstrated that two NHC-gold complexes (namely AuL4 and AuL7) possessing good anticancer activity and multi-target properties, as stated in our previous studies, play a major role in regulating the actin polymerization, by the means of in silico and in vitro assays. Using immunofluorescence and direct enzymatic assays, we proved that both the complexes inhibited the actin polymerization reaction without promoting the depolymerization of actin filaments. Our outcomes may contribute toward deepening the knowledge of NHC-gold complexes, with the objective of producing more effective and safer drugs for treating cancer diseases. Full article

We document and show a state-of-the-art methodology that could allow geoheritage sites (geosites) to become accessible to scientific and non-scientific audiences through immersive and non-immersive virtual reality applications. This is achieved through a dedicated WebGIS platform, particularly handy in communicating geoscience during the COVID-19 era. For this application, we selected nine volcanic outcrops in Santorini, Greece. The latter are mainly associated with several geological processes (e.g., dyking, explosive, and effusive eruptions). In particular, they have been associated with the famous Late Bronze Age (LBA) eruption, which made them ideal for geoheritage popularization objectives since they combine scientific and educational purposes with geotourism applications. Initially, we transformed these stunning volcanological outcrops into geospatial models—the so called virtual outcrops (VOs) here defined as virtual geosites (VGs)—through UAV-based photogrammetry and 3D modeling. In the next step, we uploaded them on an online platform that is fully accessible for Earth science teaching and communication. The nine VGs are currently accessible on a PC, a smartphone, or a tablet. Each one includes a detailed description and plenty of annotations available for the viewers during 3D exploration. We hope this work will be regarded as a forward model application for Earth sciences’ popularization and make geoheritage open to the scientific community and the lay public. Full article

Assessing the suitability of urban and agricultural land is essential for planning sustainable urban and agricultural systems. The main objective of this study is to evaluate the suitability of land in Ioannina plain (western Greece) concerning the soil contents of two potentially toxic elements, cadmium (Cd) and cobalt (Co). Geochemical and spatial analysis methods were applied to assess the distribution of Cd and Co in the soil of the Ioannina plain and identify their origin. The primary anthropogenic sources of Cd and Co in the topsoil of the study area can be attributed to traffic emissions, aircraft operations, vehicle crushing and dismantling activities. Element content is compared to international guidelines and screening values. Cadmium and Co concentration in the soil of the study area is well above the European topsoil mean. Thus, the urban and agricultural lands cover the vast majority (92%) of the total area. Cadmium concentration in soil of the study area with a mean (mg kg⁻¹) 1.7 and 2.0 was observed in agricultural and urban land use, respectively. Cobalt content in soil of the area studied with a mean (mg kg⁻¹) 30.8 and 37.1 was recorded in agricultural and urban land use, respectively. Land evaluation suitability by adopting criteria provided from the international literature is discussed. Full article

The current research on concrete and cementitious materials focuses on finding sustainable solutions to address critical issues, such as increased carbon emissions, or corrosion attack associated with reinforced concrete structures. Geopolymer concrete is considered to be an eco-friendly alternative due to its superior properties in terms of reduced carbon emissions and durability. Similarly, the use of fibre-reinforced polymer (FRP) bars to address corrosion attack in steel-reinforced structures is also gaining momentum. This paper investigates the bond performance of a newly developed self-compacting geopolymer concrete (SCGC) reinforced with basalt FRP (BFRP) bars. This study examines the bond behaviour of BFRP-reinforced SCGC specimens with variables such as bar diameter (6 mm and 10 mm) and embedment lengths. The embedment lengths adopted are 5, 10, and 15 times the bar diameter (d_b), and are denoted as 5 d_b, 10 d_b, and 15 d_b throughout the study. A total of 21 specimens, inclusive of the variable parameters, are subjected to direct pull-out tests in order to assess the bond between the rebar and the concrete. The result is then compared with the SCGC reinforced with traditional steel bars, in accordance with the ACI 440.3R-04 and CAN/CSA-S806-02 guidelines. A prediction model for bond strength has been proposed using artificial neural network (ANN) tools, which contributes to the new knowledge on the use of Basalt FRP bars as internal reinforcement in an ambient-cured self-compacting geopolymer concrete. Full article

Wire arc additive manufacturing (WAAM) is a direct energy deposition (DED) process with high deposition rates, but deformation and distortion can occur due to the high energy input and resulting strains. Despite great efforts, the prediction of distortion and resulting geometry in additive manufacturing processes using WAAM remains challenging. In this work, an artificial neural network (ANN) is established to predict welding distortion and geometric accuracy for multilayer WAAM structures. For demonstration purposes, the ANN creation process is presented on a smaller scale for multilayer beads on plate welds on a thin substrate sheet. Multiple concepts for the creation of ANNs and the handling of outliers are developed, implemented, and compared. Good results have been achieved by applying an enhanced ANN using deformation and geometry from the previously deposited layer. With further adaptions to this method, a prediction of additive welded structures, geometries, and shapes in defined segments is conceivable, which would enable a multitude of applications for ANNs in the WAAM-Process, especially for applications closer to industrial use cases. It would be feasible to use them as preparatory measures for multi-segmented structures as well as an application during the welding process to continuously adapt parameters for a higher resulting component quality. Full article

The evolution of smart water grids leads to new Big Data challenges boosting the development and application of Machine Learning techniques to support efficient and sustainable drinking water management. These powerful techniques rely on hyperparameters making the models’ tuning a tricky and crucial task. We hence propose an insightful analysis of the tuning of Artificial Neural Networks for drinking water demand forecasting. This study focuses on layers and nodes’ hyperparameters fitting of different Neural Network architectures through a grid search method by varying dataset, prediction horizon and set of inputs. In particular, the architectures involved are the Feed Forward Neural Network, the Long Short Term Memory, the Simple Recurrent Neural Network and the Gated Recurrent Unit, while the prediction interval ranges from 1 h to 1 week. To avoid the problem of the Neural Networks tuning stochasticity, we propose the selection of the median model among several repetitions for each hyperparameter’s configurations. The proposed iterative tuning procedure highlights the change of the required number of layers and nodes depending on Neural Network architectures, prediction horizon and dataset. Significant trends and considerations are pointed out to support Neural Network application in drinking water prediction. Full article

The gradual deterioration of train wheels can increase the risk of failure and lead to a higher rate of track deterioration, resulting in less reliable railway systems with higher maintenance costs. Early detection of potential wheel damages allows railway infrastructure managers to control railway operators, leading to lower infrastructure maintenance costs. This study focuses on identifying the type of sensors that can be adopted in a wayside monitoring system for wheel flat detection, as well as their optimal position. The study relies on a 3D numerical simulation of the train-track dynamic response to the presence of wheel flats. The shear and acceleration measurement points were defined in order to examine the sensitivity of the layout schemes not only to the type of sensors (strain gauge and accelerometer) but also to the position where they are installed. By considering the shear and accelerations evaluated in 19 positions of the track as inputs, the wheel flat was identified by the envelope spectrum approach using spectral kurtosis analysis. The influence of the type of sensors and their location on the accuracy of the wheel flat detection system is analyzed. Two types of trains were considered, namely the Alfa Pendular passenger vehicle and a freight wagon. Full article

The architecture of spaces for people on the autistic spectrum is evolving toward inclusive design, which should fit the requirements for independent, autonomous living, and proper support for relatives and caregivers. The use of smart sensor systems represents a valuable support to internal design in order to achieve independent living for impaired people. Accordingly, these devices can monitor or prevent hazardous situations, ensuring security and privacy. Acoustic sensor systems, for instance, could be used in order to realize a passive monitoring system. The correct functioning of such devices needs optimal indoor acoustic criteria. Nevertheless, these criteria should also comply with dedicated acoustic requests that autistic individuals with hearing impairment or hypersensitivity to sound could need. Thus, this research represents the first attempt to balance, integrate, and develop these issues, presenting (i) a wide literature overview related to both topics, (ii) a focused analysis on real facility, and (iii) a final optimization, which takes into account, merges, and elucidates all the presented unsolved issues. Full article

Attention-based methods for deep neural networks constitute a technique that has attracted increased interest in recent years. Attention mechanisms can focus on important parts of a sequence and, as a result, enhance the performance of neural networks in a variety of tasks, including sentiment analysis, emotion recognition, machine translation and speech recognition. In this work, we study attention-based models built on recurrent neural networks (RNNs) and examine their performance in various contexts of sentiment analysis. Self-attention, global-attention and hierarchical-attention methods are examined under various deep neural models, training methods and hyperparameters. Even though attention mechanisms are a powerful recent concept in the field of deep learning, their exact effectiveness in sentiment analysis is yet to be thoroughly assessed. A comparative analysis is performed in a text sentiment classification task where baseline models are compared with and without the use of attention for every experiment. The experimental study additionally examines the proposed models’ ability in recognizing opinions and emotions in movie reviews. The results indicate that attention-based models lead to great improvements in the performance of deep neural models showcasing up to a 3.5% improvement in their accuracy. Full article

Subsea pipelines carry oil or natural gas over long distances of the seabed, but fluid leakage due to a failure of the pipeline can culminate in huge environmental disasters. Scouring process may take place beneath pipelines due to current and/or wave action, causing pipeline suspension and leading to the risk of pipeline failure. The resulting morphological variations of the seabed propagate not only below and normally to the pipeline but also along the pipeline itself. Therefore, 3D scouring patterns need to be considered. Mainly based on the experimental works at laboratory scale by Cheng and coworkers, in this study, Artificial Intelligent (AI) techniques are employed to present new equations for predicting three dimensional current- and wave-induced scour rates around subsea pipelines. These equations are given in terms of key dimensionless parameters, among which are the Shields’ parameter, the Keulegan–Carpenter number, relative embedment depth, and wave/current angle of attach. Using various statistical benchmarks, the efficiency of AI-models-based regression equations is assessed. The proposed predictive models perform much better than the existing empirical equations from literature. Even more interestingly, they exhibit a clear physical consistence and allow for highlighting the relative importance of the key dimensionless variables governing the scouring patterns. Full article