Editor’s Choice Articles

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

Magnetite (Fe₃O₄) nanoparticles (NPs) are attractive nanomaterials in the field of material science, chemistry, and physics because of their valuable properties, such as soft ferromagnetism, half-metallicity, and biocompatibility. Various structures of Fe₃O₄ NPs with different sizes, geometries, and nanoarchitectures have been synthesized, and the related properties have been studied with targets in multiple fields of applications, including biomedical devices, electronic devices, environmental solutions, and energy applications. Tailoring the sizes, geometries, magnetic properties, and functionalities is an important task that determines the performance of Fe₃O₄ NPs in many applications. Therefore, this review focuses on the crucial aspects of Fe₃O₄ NPs, including structures, synthesis, magnetic properties, and strategies for functionalization, which jointly determine the application performance of various Fe₃O₄ NP-based systems. We first summarize the recent advances in the synthesis of magnetite NPs with different sizes, morphologies, and magnetic properties. We also highlight the importance of synthetic factors in controlling the structures and properties of NPs, such as the uniformity of sizes, morphology, surfaces, and magnetic properties. Moreover, emerging applications using Fe₃O₄ NPs and their functionalized nanostructures are also highlighted with a focus on applications in biomedical technologies, biosensing, environmental remedies for water treatment, and energy storage and conversion devices. 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 growing population and increasing demand for surface transportation have highlighted the importance of maintaining safe and reliable civil infrastructures for daily use. Among all civil infrastructures, bridges are one of the most important elements in the transportation system. As such, to prevent any failures caused by aging and environmental impacts, bridges require periodic inspections. This becomes even more critical due to climate change and its effect on bridges, especially in the coastal regions. Most of the inspections conducted incorporate the visual type of evaluation due to its simplicity. However, with the current developments in new technologies, there is a need for more advanced techniques of structural health monitoring (SHM) methods to be incorporated in the maintenance programs for more accurate and efficient surveys. In this paper, non-destructive testing (NDT) methods applicable to steel bridges are reviewed, with a focus on methods applicable to local damage detection. Moreover, the methodology, advantages and disadvantages, and up-to-date research on NDT methods are presented. Furthermore, the application of novel NDT techniques using innovative sensors, drones, and robots for the rapid and efficient assessment of damages on small and large scales is emphasized. This study is deemed necessary as it compiles in one place the available information regarding NDT methods for in-service steel bridges. Access to such information is critical for researchers who intend to work on new or improved NDT techniques. 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

The Building Information Modeling (BIM) methodology improves architectural and infrastructure projects by digitizing their processes throughout their life cycle stages, such as design, construction, management, monitoring, and operation. In recent years, the automation of these processes has been favored by the use of visual programming (VP) tools that have replaced conventional programming languages for visual schemes. The use of these tools in architectural projects is becoming increasing popular. However, this is not the case in infrastructure projects, for which the use of VP algorithms remains scarce. The aim of this work is to encourage both scholars and engineers to implement VP tools in infrastructure projects. For this purpose, this work reviews, for the first time in the literature, the state-of-the-art and future research trends of VP tools in infrastructure projects. 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

Climate change mitigation, the goal of reducing CO₂ emissions, more stringent regulations and the increment in energy costs have pushed researchers to study energy efficiency and renewable energy sources. Manufacturing systems are large energy consumers and are thus responsible for huge greenhouse gas emissions; for these reasons, many studies have focused on this topic recently. This review aims to summarize the most important papers on energy efficiency and renewable energy sources in manufacturing systems published in the last fifteen years. The works are grouped together, considering the system typology, i.e., manufacturing system subclasses (single machine, flow shop, job shop, etc.) or the assembly line, the developed energy-saving policies and the implementation of the renewable energy sources in the studied contexts. A description of the main approaches used in the analyzed papers was discussed. The conclusion reports the main findings of the review and suggests future directions for the researchers in the integration of renewable energy in the manufacturing systems consumption models. 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 recent years, the use of digital twins (DT) to improve maintenance procedures has increased in various industrial sectors (e.g., manufacturing, energy industry, aerospace) but is more limited in the construction industry. However, the operation and maintenance (O&M) phase of a building’s life cycle is the most expensive. Smart buildings already use BIM (Building Information Modeling) for facility management, but they lack the predictive capabilities of DT. On the other hand, the use of extended reality (XR) technologies to improve maintenance operations has been a major topic of academic research in recent years, both through data display and remote collaboration. In this context, this paper focuses on reviewing projects using a combination of these technologies to improve maintenance operations in smart buildings. This review uses a combination of at least three of the terms “Digital Twin”, “Maintenance”, “BIM” and “Extended Reality”. Results show how a BIM can be used to create a DT and how this DT use combined with XR technologies can improve maintenance operations in a smart building. This paper also highlights the challenges for the correct implementation of a BIM-based DT combined with XR devices. An example of use is also proposed using a diagram of the possible interactions between the user, the DT and the application framework during maintenance operations. 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

With the advent of the digital computer, time series analysis has gained wide attention and is being applied to many fields of science. This paper reviews many traditional and recent techniques for time series analysis and change detection, including spectral and wavelet analyses with their advantages and weaknesses. First, Fourier and least-squares-based spectral analysis methods and spectral leakage attenuation methods are reviewed. Second, several time-frequency decomposition methods are described in detail. Third, several change or breakpoints detection methods are briefly reviewed. Finally, some of the applications of the methods in various fields, such as geodesy, geophysics, remote sensing, astronomy, hydrology, finance, and medicine, are listed in a table. The main focus of this paper is reviewing the most recent methods for analyzing non-stationary time series that may not be sampled at equally spaced time intervals without the need for any interpolation prior to the analysis. Understanding the methods presented herein is worthwhile to further develop and apply them for unraveling our universe. Full article

Remanufacturing is a domain that has increasingly been exploited during recent years due to its numerous advantages and the increasing need for society to promote a circular economy leading to sustainability. Remanufacturing is one of the main end-of-life (EoL) options that can lead to a circular economy. There is therefore a strong need to prioritize this option over other available options at the end-of-life stage of a product because it is the only recovery option that maintains the same quality as that of a new product. This review focuses on the different lifecycle strategies that can help improve remanufacturing; in other words, the various strategies prior to, during or after the end-of-life of a product that can increase the chances of that product being remanufactured rather than being recycled or disposed of after its end-of-use. The emergence of the fourth industrial revolution, also known as industry 4.0 (I4.0), will help enhance data acquisition and sharing between different stages in the supply chain, as well boost smart remanufacturing techniques. This review examines how strategies like design for remanufacturing (DfRem), remaining useful life (RUL), product service system (PSS), closed-loop supply chain (CLSC), smart remanufacturing, EoL product collection and reverse logistics (RL) can enhance remanufacturing. We should bear in mind that not all products can be remanufactured, so other options are also considered. This review mainly focuses on products that can be remanufactured. For this review, we used 181 research papers from three databases; Science Direct, Web of Science and Scopus. 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

In recent years; the interaction of nanoparticles (NPs) with plants has been intensively studied. Therefore, more and more aspects related to both the positive and negative impact of NP on plants are well described. This article focuses on two aspects of NP interaction with plants. The first is a summary of the current knowledge on NP migration through the roots into the plant body, in particular, the role of the cell wall. The second aspect summarizes the current knowledge of the participation of the symplast, including the plasmodesmata (PD), in the movement of NP within the plant body. We highlight the gaps in our knowledge of the plant–NP interactions; paying attention to the need for future studies to explain the mechanisms that regulate the composition of the cell wall and the functioning of the PD under the influence of NP. 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

Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs. Full article

Impaired wound healing is an encumbering public health issue that increases the demand for developing new therapies in order to minimize health costs and enhance treatment efficacy. Available conventional therapies are still unable to maximize their potential in penetrating the skin at the target site and accelerating the healing process. Nanotechnology exhibits an excellent opportunity to enrich currently available medical treatments, enhance standard care and manage wounds. It is a promising approach, able to address issues such as the permeability and bioavailability of drugs with reduced stability or low water solubility. This paper focuses on nanosized-lipid-based drug delivery systems, describing their numerous applications in managing skin wounds. We also highlight the relationship between the physicochemical characteristics of nanosized, lipid-based drug delivery systems and their impact on the wound-healing process. Different types of nanosized-lipid-based drug delivery systems, such as vesicular systems and lipid nanoparticles, demonstrated better applicability and enhanced skin penetration in wound healing therapy compared with conventional treatments. Moreover, an improved chemically and physically stable drug delivery system, with increased drug loading capacity and enhanced bioavailability, has been shown in drugs encapsulated in lipid nanoparticles. Their applications in wound care show potential for overcoming impediments, such as the inadequate bioavailability of active agents with low solubility. Future research in nanosized-lipid-based drug delivery systems will allow the achievement of increased bioavailability and better control of drug release, providing the clinician with more effective therapies for wound care. Full article

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods. 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

In recent years, cold atmospheric pressure plasma (CAPP) technology has received substantial attention due to its valuable properties including operational simplicity, low running cost, and environmental friendliness. Several different gases (air, nitrogen, helium, argon) and techniques (corona discharge, dielectric barrier discharge, plasma jet) can be used to generate plasma at atmospheric pressure and low temperature. Plasma treatment is routinely used in materials science to modify the surface properties (e.g., wettability, chemical composition, adhesion) of a wide range of materials (e.g., polymers, textiles, metals, glasses). Moreover, CAPP seems to be a powerful tool for the inactivation of various pathogens (e.g., bacteria, fungi, viruses) in the food industry (e.g., food and packing material decontamination, shelf life extension), agriculture (e.g., disinfection of seeds, fertilizer, water, soil) and medicine (e.g., sterilization of medical equipment, implants). Plasma medicine also holds great promise for direct therapeutic treatments in dentistry (tooth bleaching), dermatology (atopic eczema, wound healing) and oncology (melanoma, glioblastoma). Overall, CAPP technology is an innovative, powerful and effective tool offering a broad application potential. However, its limitations and negative impacts need to be determined in order to receive regulatory approval and consumer acceptance. 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

The performance of bituminous materials is mainly affected by the prevailing maximum and minimum temperatures, and their mechanical properties can vary significantly with the magnitude of the temperature changes. The given effect can be observed from changes occurring in the bitumen or asphalt mixture stiffness and the materials’ serviceable life. Furthermore, when asphalt pavement layer are used, the temperature changes can be credited to climatic factors such as air temperature, solar radiation and wind. Thus in relevance to the discussed issue, the contents of this paper displays a comprehensive review of the collected existing 38 prediction models and broadly classifies them into their corresponding numerical, analytical and statistical models. These models further present different formulas based on the climate, environment, and methods of data collection and analyses. Corresponding to which, most models provide reasonable predictions for both minimum and maximum pavement temperatures. Some models can even predict the temperature of asphalt pavement layers on an hourly or daily basis using the provided statistical method. The analytical models can provide straight-forward solutions, but assumptions on boundary conditions should be simplified. Critical climatic and pavement factors influencing the accuracy of predicting temperature were examined. This paper recommends future studies involving coupled heat transfer model for the pavement and the environment, particularly consider to be made on the impact of surface water and temperature of pavements in urban areas. 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

A digital twin of a manufacturing system is a digital copy of the physical manufacturing system that consists of various digital models at multiple scales and levels. Digital twins that communicate with their physical counterparts throughout their lifecycle are the basis for data-driven factories. The problem with developing digital models that form the digital twin is that they operate with large amounts of heterogeneous data. Since the models represent simplifications of the physical world, managing the heterogeneous data and linking the data with the digital twin represent a challenge. The paper proposes a five-step approach to planning data-driven digital twins of manufacturing systems and their processes. The approach guides the user from breaking down the system and the underlying building blocks of the processes into four groups. The development of a digital model includes predefined necessary parameters that allow a digital model connecting with a real manufacturing system. The connection enables the control of the real manufacturing system and allows the creation of the digital twin. Presentation and visualization of a system functioning based on the digital twin for different participants is presented in the last step. The suitability of the approach for the industrial environment is illustrated using the case study of planning the digital twin for material logistics of the manufacturing system. Full article

The healing power of light has attracted interest for thousands of years. Scientific discoveries and technological advancements in the field have eventually led to the emergence of photodynamic therapy, which soon became a promising approach in treating a broad range of diseases. Based on the interaction between light, molecular oxygen, and various photosensitizers, photodynamic therapy represents a non-invasive, non-toxic, repeatable procedure for tumor treatment, wound healing, and pathogens inactivation. However, classic photosensitizing compounds impose limitations on their clinical applications. Aiming to overcome these drawbacks, nanotechnology came as a solution for improving targeting efficiency, release control, and solubility of traditional photosensitizers. This paper proposes a comprehensive path, starting with the photodynamic therapy mechanism, evolution over the years, integration of nanotechnology, and ending with a detailed review of the most important applications of this therapeutic approach. Full article

Various reactive oxygen and nitrogen species are accompanied by electrons, ultra-violet (UV) radiation, ions, photons, and electric fields in non-thermal atmospheric pressure plasma. Plasma technology is already used in diverse fields, such as biomedicine, dentistry, agriculture, ozone generation, chemical synthesis, surface treatment, and coating. Non-thermal atmospheric pressure plasma is also considered a promising technology in environmental pollution control. The degradation of organic and inorganic pollutants will be massively advanced by plasma-generated reactive species. Various investigations on the use of non-thermal atmospheric pressure plasma technology for organic wastewater purification have already been performed, and advancements are continuing to be made in this area. This work provides a critical review of the ongoing improvements related to the use of non-thermal plasma in wastewater control and outlines the operational principle, standards, parameters, and boundaries with a special focus on the degradation of organic compounds in wastewater treatment. Full article

On 30 October 2020, an Mw = 7.0 earthquake struck the eastern Aegean Sea. It triggered earthquake environmental effects (EEEs) on Samos Island detected by field surveys, relevant questionnaires, and Interferometric Synthetic Aperture Radar (InSAR) analysis. The primary EEEs detected in the field comprise coseismic uplift imprinted on rocky coasts and port facilities around Samos and coseismic surface ruptures in northern Samos. The secondary EEEs were mainly observed in northern Samos and include slope failures, liquefaction, hydrological anomalies, and ground cracks. With the contribution of the InSAR, subsidence was detected and slope movements were also identified in inaccessible areas. Moreover, the type of the surface deformation detected by InSAR is qualitatively identical to field observations. As regards the EEE distribution, effects were generated in all fault blocks. By applying the Environmental Seismic Intensity (ESI-07) scale, the maximum intensities were observed in northern Samos. Based on the results from the applied methods, it is suggested that the northern and northwestern parts of Samos constitute an almost 30-km-long coseismic deformation zone characterized by extensive primary and secondary EEEs. The surface projection of the causative offshore northern Samos fault points to this zone, indicating a depth–surface connection and revealing a significant role in the rupture propagation. Full article

This paper presents a mathematical continuum model to investigate the static stability buckling of cross-ply single-walled (SW) carbon nanotube reinforced composite (CNTRC) curved sandwich nanobeams in thermal environment, based on a novel quasi-3D higher-order shear deformation theory. The study considers possible nano-scale size effects in agreement with a nonlocal strain gradient theory, including a higher-order nonlocal parameter (material scale) and gradient length scale (size scale), to account for size-dependent properties. Several types of reinforcement material distributions are assumed, namely a uniform distribution (UD) as well as X- and O- functionally graded (FG) distributions. The material properties are also assumed to be temperature-dependent in agreement with the Touloukian principle. The problem is solved in closed form by applying the Galerkin method, where a numerical study is performed systematically to validate the proposed model, and check for the effects of several factors on the buckling response of CNTRC curved sandwich nanobeams, including the reinforcement material distributions, boundary conditions, length scale and nonlocal parameters, together with some geometry properties, such as the opening angle and slenderness ratio. The proposed model is verified to be an effective theoretical tool to treat the thermal buckling response of curved CNTRC sandwich nanobeams, ranging from macroscale to nanoscale, whose examples could be of great interest for the design of many nanostructural components in different engineering applications. Full article

Background: The application of virtual and augmented reality technologies to orthopaedic surgery training and practice aims to increase the safety and accuracy of procedures and reducing complications and costs. The purpose of this systematic review is to summarise the present literature on this topic while providing a detailed analysis of current flaws and benefits. Methods: A comprehensive search on the PubMed, Cochrane, CINAHL, and Embase database was conducted from inception to February 2021. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used to improve the reporting of the review. The Cochrane Risk of Bias Tool and the Methodological Index for Non-Randomized Studies (MINORS) was used to assess the quality and potential bias of the included randomized and non-randomized control trials, respectively. Results: Virtual reality has been proven revolutionary for both resident training and preoperative planning. Thanks to augmented reality, orthopaedic surgeons could carry out procedures faster and more accurately, improving overall safety. Artificial intelligence (AI) is a promising technology with limitless potential, but, nowadays, its use in orthopaedic surgery is limited to preoperative diagnosis. Conclusions: Extended reality technologies have the potential to reform orthopaedic training and practice, providing an opportunity for unidirectional growth towards a patient-centred approach. Full article

Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as “pre-training”), versatile and performing models are released continuously for every new network design. These networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have been found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. In addition, available resources are identified and a strategy to deal with bias in deep NLP is proposed. Full article

Due to its pervasive nature, the Internet of Things (IoT) is demanding for Low Power Wide Area Networks (LPWAN) since wirelessly connected devices need battery-efficient and long-range communications. Due to its low-cost and high availability (regional/city level scale), this type of network has been widely used in several IoT applications, such as Smart Metering, Smart Grids, Smart Buildings, Intelligent Transportation Systems (ITS), SCADA Systems. By using LPWAN technologies, the IoT devices are less dependent on common and existing infrastructure, can operate using small, inexpensive, and long-lasting batteries (up to 10 years), and can be easily deployed within wide areas, typically above 2 km in urban zones. The starting point of this work was an overview of the security vulnerabilities that exist in LPWANs, followed by a literature review with the main goal of substantiating an attack vector analysis specifically designed for the IoT ecosystem. This methodological approach resulted in three main contributions: (i) a systematic review regarding cybersecurity in LPWANs with a focus on vulnerabilities, threats, and typical defense strategies; (ii) a state-of-the-art review on the most prominent results that have been found in the systematic review, with focus on the last three years; (iii) a security analysis on the recent attack vectors regarding IoT applications using LPWANs. Results have shown that LPWANs communication technologies contain security vulnerabilities that can lead to irreversible harm in critical and non-critical IoT application domains. Also, the conception and implementation of up-to-date defenses are relevant to protect systems, networks, and data. Full article

1Cr11Ni2W2MoV is a new martensitic heat-resistant stainless steel utilized in the manufacturing of aero-engine high-temperature bearing components. Welding of this type of steel using fusion welding techniques causes many defects. Friction stir welding (FSW) is a valuable alternative. However, few investigations have been performed on the FSW of steels because of the high melting point and the costly tools. Numerical simulation in this regard is a cost-effective solution for the FSW of this steel in order to optimize the parameters and to reduce the number of experiments for obtaining high-quality joints. In this study, a 3D thermo-mechanical finite element model based on the Coupled Eulerian Lagrangian (CEL) approach was developed to study the FSW of 1Cr11Ni2W2MoV steel. Numerical results of metallurgical zones’ shape and weld appearance at different tool rotation rates of 250, 350, 450 and 550 rpm are in good agreement with the experimental results. The results revealed that the peak temperature, plastic strain, surface roughness and flash size increased with an increase in the tool rotation rate. Lack-of-fill defect was produced at the highest tool rotation rate of 650 rpm. Moreover, an asymmetrical stir zone was produced at a high tool rotation rate. Full article

This paper presents an efficient mesoscale simulation of a Laser Powder Bed Fusion (LPBF) process using the Smoothed Particle Hydrodynamics (SPH) method. The efficiency lies in reducing the computational effort via spatial adaptivity, for which a dynamic particle refinement pattern with an optimized neighbor-search algorithm is used. The melt pool dynamics is modeled by resolving the thermal, mechanical, and material fields in a single laser track application. After validating the solver by two benchmark tests where analytical and experimental data are available, we simulate a single-track LPBF process by adopting SPH in multi resolutions. The LPBF simulation results show that the proposed adaptive refinement with and without an optimized neighbor-search approach saves almost 50% and 35% of the SPH calculation time, respectively. This achievement enables several opportunities for parametric studies and running high-resolution models with less computational effort. Full article

To achieve efficient personalized production at an affordable cost, a modular manufacturing system (MMS) can be utilized. MMS enables restructuring of its configuration to accommodate product changes and is thus an efficient solution to reduce the costs involved in personalized production. A micro smart factory (MSF) is an MMS with heterogeneous production processes to enable personalized production. Similar to MMS, MSF also enables the restructuring of production configuration; additionally, it comprises cyber-physical production systems (CPPSs) that help achieve resilience. However, MSFs need to overcome performance hurdles with respect to production control. Therefore, this paper proposes a digital twin (DT) and reinforcement learning (RL)-based production control method. This method replaces the existing dispatching rule in the type and instance phases of the MSF. In this method, the RL policy network is learned and evaluated by coordination between DT and RL. The DT provides virtual event logs that include states, actions, and rewards to support learning. These virtual event logs are returned based on vertical integration with the MSF. As a result, the proposed method provides a resilient solution to the CPPS architectural framework and achieves appropriate actions to the dynamic situation of MSF. Additionally, applying DT with RL helps decide what-next/where-next in the production cycle. Moreover, the proposed concept can be extended to various manufacturing domains because the priority rule concept is frequently applied. Full article

The strength of unsaturated soil is defined by the soil water retention behavior and soil suction acting inside the soil matrix. In order to obtain the suction and moisture profile in the vadose zone, specific measuring techniques are needed. Time domain reflectometry (TDR) conventionally measures moisture at individual points only. Therefore, spatial time domain reflectometry (spatial TDR) was developed for characterizing the moisture content profile along the unsaturated soil strata. This paper introduces an experimental set-up used for measuring dynamic moisture profiles with high spatial and temporal resolution. The moisture measurement method is based on inverse modeling the telegraph equation with a capacitance model of soil/sensor environment using an optimization technique. With the addition of point-wise soil suction measurement using tensiometers, the soil water retention curve (SWRC) can be derived in the transient flow condition instead of the static or steady-state condition usually applied for conventional testing methodologies. The experiment was successfully set up and conducted with thorough validations to demonstrate the functionalities in terms of detecting dynamic moisture profiles, dynamic soil suction, and outflow seepage flux under transient flow condition. Furthermore, some TDR measurements are presented with a discussion referring to the inverse analysis of TDR traces for extracting the dielectric properties of soil. The detected static SWRC is finally compared to the static SWRC measured by the conventional method. The preliminary outcomes underpin the success of applying the spatial TDR technique and also demonstrate several advantages of this platform for investigating the unsaturated soil seepage issue under transient flow conditions. Full article

In the Industry 4.0 era, digital tools applied to production and manufacturing activities represent a challenge for companies. Digital Twin (DT) technology is based on the integration of different “traditional” tools, such as simulation modeling and sensors, and is aimed at increasing process performance. In DTs, simulation modeling allows for the building of a digital copy of real processes, which is dynamically updated through data derived from smart objects based on sensor technologies. The use of DT within manufacturing activities is constantly increasing, as DTs are being applied in different areas, from the design phase to the operational ones. This study aims to analyze existing fields of applications of DTs for supporting safety management processes in order to evaluate the current state of the art. A bibliometric review was carried out through VOSviewer to evaluate studies and applications of DTs in the engineering and computer science areas and to identify research clusters and future trends. Next, a bibliometric and systematic review was carried out to deepen the relation between the DT approach and safety issues. The findings highlight that in recent years, DT applications have been tested and developed to support operators during normal and emergency conditions and to enhance their abilities to control safety levels. Full article

The surface defects’ region of strip steel is small, and has various defect types and, complex gray structures. There tend to be a large number of false defects and edge light interference, which lead traditional machine vision algorithms to be unable to detect defects for various types of strip steel. Image detection techniques based on deep learning require a large number of images to train a network. However, for a dataset with few samples with category imbalanced defects, common deep learning neural network training tasks cannot be carried out. Based on rapid image preprocessing algorithms (improved gray projection algorithm, ROI image augmentation algorithm) and transfer learning theory, this paper proposes a set of processes for complete strip steel defect detection. These methods achieved surface rapid screening, defect feature extraction, sample dataset’s category balance, data augmentation, defect detection, and classification. Through verification of the mixed dataset, composed of the NEU surface dataset and dataset in this paper, the recognition accuracy of the improved VGG19 network in this paper reached 97.8%. The improved VGG19 network performs slightly better than the baseline VGG19 in six types of defects, but the improved VGG19 performs significantly better in the surface seams defects. The convergence speed and accuracy of the improved VGG19 network were taken into account, and the detection rate was greatly improved with few samples and imbalanced datasets. This paper also has practical value in terms of extending its method of strip steel defect detection to other products. Full article

Nanotechnology plays a big part in our modern daily lives, ranging from the biomedical sector to the energy sector. There are different physicochemical and biological methods to synthesise nanoparticles towards multiple applications. Biogenic production of nanoparticles through the utilisation of microorganisms provides great advantages over other techniques and is increasingly being explored. This review examines the process of the biogenic synthesis of nanoparticles mediated by microorganisms such as bacteria, fungi and algae, and their applications. Microorganisms offer a disparate environment for nanoparticle synthesis. Optimum production and minimum time to obtain the desired size and shape, to improve the stability of nanoparticles and to optimise specific microorganisms for specific applications are the challenges to address, however. Numerous applications of biogenic nanoparticles in medicine, environment, drug delivery and biochemical sensors are discussed. Full article

A driving technical concern for the automobile industry is their assurance that developed windshield products meet Federal safety standards. Besides conducting innumerable glass breakage experiments, product developers also have the option of utilizing numerical approaches that can provide further insight into glass impact breakage, fracture, and fragmentation. The combined finite-discrete element method (FDEM) is one such tool and was used in this study to investigate 3D impact glass fracture processes. To enable this analysis, a generalized traction-separation model, which defines the constitutive relationship between the traction and separation in FDEM cohesive zone models, was introduced. The mechanical responses of a laminated glass and a glass plate under impact were then analyzed. For laminated glass, an impact fracture process was investigated and results were compared against corresponding experiments. Correspondingly, two glass plate impact fracture patterns, i.e., concentric fractures and radial fractures, were simulated. The results show that for both cases, FDEM simulated fracture processes and fracture patterns are in good agreement with the experimental observations. The work demonstrates that FDEM is an effective tool for modeling of fracture and fragmentation in glass. Full article

Deep and meaningful learning (DML) in distant education should be an essential outcome of quality education. In this literature review, we focus on e-learning effectiveness along with the factors and conditions leading to DML when using social virtual reality environments (SVREs) in distance mode higher education (HE). Hence, a systematic literature review was conducted summarizing the findings from thirty-three empirical studies in HE between 2004 (appearance of VR) and 2019 (before coronavirus appearance). We searched for the cognitive, social, and affective aspects of DML in a research framework and studied their weight in SVREs. The findings suggest that the use of SVREs can provide authentic, simulated, cognitively challenging experiences in engaging, motivating environments for open-ended social and collaborative interactions and intentional, personalized learning. Furthermore, the findings indicate that educators and SVRE designers need to place more emphasis on the socio-cultural semiotics and emotional aspects of e-learning and ethical issues such as privacy and security. The mediating factors for DML in SVREs were accumulated and classified in the resultant Blended Model for Deep and Meaningful e-learning in SVREs. Improvement recommendations include meaningful contexts, purposeful activation, learner agency, intrinsic emotional engagement, holistic social integration, and meticulous user obstacle removal. Full article

Accurate computational predictions of band gaps are of practical importance to the modeling and development of semiconductor technologies, such as (opto)electronic devices and photoelectrochemical cells. Among available electronic-structure methods, density-functional theory (DFT) with the Hubbard U correction (DFT+U) applied to band edge states is a computationally tractable approach to improve the accuracy of band gap predictions beyond that of DFT calculations based on (semi)local functionals. At variance with DFT approximations, which are not intended to describe optical band gaps and other excited-state properties, DFT+U can be interpreted as an approximate spectral-potential method when U is determined by imposing the piecewise linearity of the total energy with respect to electronic occupations in the Hubbard manifold (thus removing self-interaction errors in this subspace), thereby providing a (heuristic) justification for using DFT+U to predict band gaps. However, it is still frequent in the literature to determine the Hubbard U parameters semiempirically by tuning their values to reproduce experimental band gaps, which ultimately alters the description of other total-energy characteristics. Here, we present an extensive assessment of DFT+U band gaps computed using self-consistent ab initio U parameters obtained from density-functional perturbation theory to impose the aforementioned piecewise linearity of the total energy. The study is carried out on 20 compounds containing transition-metal or p-block (group III-IV) elements, including oxides, nitrides, sulfides, oxynitrides, and oxysulfides. By comparing DFT+U results obtained using nonorthogonalized and orthogonalized atomic orbitals as Hubbard projectors, we find that the predicted band gaps are extremely sensitive to the type of projector functions and that the orthogonalized projectors give the most accurate band gaps, in satisfactory agreement with experimental data. This work demonstrates that DFT+U may serve as a useful method for high-throughput workflows that require reliable band gap predictions at moderate computational cost. Full article

Simulation-based medical training is considered an effective tool to acquire/refine technical skills, mitigating the ethical issues of Halsted’s model. This review aims at evaluating the literature on medical simulation techniques based on augmented reality (AR), mixed reality (MR), and hybrid approaches. The research identified 23 articles that meet the inclusion criteria: 43% combine two approaches (MR and hybrid), 22% combine all three, 26% employ only the hybrid approach, and 9% apply only the MR approach. Among the studies reviewed, 22% use commercial simulators, whereas 78% describe custom-made simulators. Each simulator is classified according to its target clinical application: training of surgical tasks (e.g., specific tasks for training in neurosurgery, abdominal surgery, orthopedic surgery, dental surgery, otorhinolaryngological surgery, or also generic tasks such as palpation) and education in medicine (e.g., anatomy learning). Additionally, the review assesses the complexity, reusability, and realism of the physical replicas, as well as the portability of the simulators. Finally, we describe whether and how the simulators have been validated. The review highlights that most of the studies do not have a significant sample size and that they include only a feasibility assessment and preliminary validation; thus, further research is needed to validate existing simulators and to verify whether improvements in performance on a simulated scenario translate into improved performance on real patients. Full article