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Biomimetic actuators are typically constructed as functional bi- or multilayers, where actuating and resistance layers together dictate bending responses upon triggering by environmental stimuli. Inspired by motile plant structures, like the stems of the false rose of Jericho (Selaginella lepidophylla), we introduce polymer-modified paper sheets that can act as soft robotic single-layer actuators capable of hygro-responsive bending reactions. A tailored gradient modification of the paper sheet along its thickness entails increased dry and wet tensile strength and allows at the same time for hygro-responsiveness. For the fabrication of such single-layer paper devices, the adsorption behavior of a cross-linkable polymer to cellulose fiber networks was first evaluated. By using different concentrations and drying procedures fine-tuned polymer gradients throughout the thickness can be achieved. Due to the covalent cross-linking of polymer with fibers, these paper samples possess significantly increased dry and wet tensile strength properties. We furthermore investigated these gradient papers with respect to a mechanical deflection during humidity cycling. The highest humidity sensitivity is achieved using eucalyptus paper with a grammage of 150 g m⁻² modified with the polymer dissolved in IPA (~13 wt%) possessing a polymer gradient. Our study presents a straightforward approach for the design of novel hygroscopic, paper-based single-layer actuators, which have a high potential for diverse soft robotic and sensor applications.

The paper presents the results of studies on the effect of rye bran addition (from 10 to 20%) on the course of the granulation process of post-harvest sage waste and on the quality of the obtained granulate (including its fuel properties). The granulation process was tested on the SS-4 test stand, with a P-300 granulator with a “flat matrix-compacting rollers” working system. The addition of rye bran causes a reduction of the power requirement of the granulator (from 3.75 to 3.19 kW) and a reduction of physical and bulk density, as well as an increase in its kinetic durability. We determined the higher heating value of sage (at 10% moisture content), 19.39 MJ/kg, and the lower heating value, 18.17 MJ/kg, thereby allowing us to state its high energy values. The addition of 20% of rye bran causes a decrease in the higher heating value by 2.07% and the lower heating value by 2.67%.

Most of the buildings that exist today were built based on 2D drawings. Building information models that represent design-stage product information have become prevalent in the second decade of the 21st century. Still, it will take many decades before such models become the norm for all existing buildings. In the meantime, the building industry lacks the tools to leverage the benefits of digital information management for construction, operation, and renovation. To this end, this paper reviews the state-of-the-art practice and research for constructing (generating) and maintaining (updating) geometric digital twins. This paper also highlights the key limitations preventing current research from being adopted in practice and derives a new geometry-based object class hierarchy that mainly focuses on the geometric properties of building objects, in contrast to widely used existing object categorisations that are mainly function-oriented. We argue that this new class hierarchy can serve as the main building block for prioritising the automation of the most frequently used object classes for geometric digital twin construction and maintenance. We also draw novel insights into the limitations of current methods and uncover further research directions to tackle these problems. Specifically, we believe that adapting deep learning methods can increase the robustness of object detection and segmentation of various types; involving design intents can achieve a high resolution of model construction and maintenance; using images as a complementary input can help to detect transparent and specular objects; and combining synthetic data for algorithm training can overcome the lack of real labelled datasets.

New technologies are developed inside today’s companies with the ascent of Industry 4.0 paradigm; Artificial Intelligence applied to Predictive Maintenance is one of these, helping factories automate their systems in detecting anomalies. The deviation of statistical features from standard operating conditions computed on collected data is a common investigation technique that companies use. The information loss due to transformation from raw data to extracted features is a problem of this approach. Furthermore, a common Predictive Maintenance framework requires historical data about failures that often do not exist, neglecting the possibility of applying it. This paper uses Artificial Intelligence as Machine Learning models to recognize when something changes in the data’s behavior collected up to that moment, also helping companies to gather a preliminary dataset for future Predictive Maintenance implementation. The aim concerns a framework in which several sensors are used to collect data by adopting a sensor fusion approach. The architecture is composed of an optimized software system able to enhance the computation scalability and the response time regarding novelty detection. This article analyzes the proposed architecture, then explains a proof-of-concept development using a digital model; finally, two real cases are studied to show how the framework behaves in a real environment. The analysis done in this paper has an application-oriented approach; hence a company can directly use the framework in its systems.

The role of ocean energy is expected to grow rapidly in the coming years, and techno-economic analysis will play a crucial role. Nowadays, despite strong assumptions, the vast majority of studies model costs using a top-down approach (the TdA) that leads to an unrepresentative economic model. WEC developers usually go through the the TdA approach because more detailed cost data are not available at an earlier design stage. At a very advanced design stage, some studies have also proposed techno-economic optimisation based on the bottom-up approach (BuA). This entails that the detailed cost metrics presented in the literature are very specific to the WEC type (hence not applicable to other cases) or unrepresentative. This lack of easily accessible detailed cost functions in the current state of the art leads to ineffective optimisations at an earlier stage of WEC development. In this paper, a BuA for WECs is proposed that can be used for techno-economic optimisation at the early design stage. To achieve this goal, cost functions of most common components in the WEC field are retrieved from the literature, exposed, and critically compared. The large number of components considered allows the results of this work to be applied to a vast pool of WECs. The novelty of the presented cost functions is their parameterization with respect to the technological specifications, which already enables their adoption in the design optimisation phase. With the goal of quantifying the results and critically discuss the differences between the TdA and the BuA, the developed methodology and cost functions are applied to a case study and specifically adopted for the calculation of the capital cost of PeWEC (pendulum wave energy converter). In addition, a hybrid approach (HyA) is presented and discussed as an intermediate approach between the TdA and the BdA. Results are compared in terms of capital expenditure (CapEx) and pie cost distribution: the impact of adopting different cost metrics is discussed, highlighting the role that reliable cost functions can have on early stage technology development. This paper proposes more than 50 cost functions for WEC components. Referring to the case study, it is shown that while the total cost differs only slightly (11%), the pie distribution changes by up to 22%. Mooring system and power take-off are the cost items where the TdA and the HyA differ more from the BuA cost estimate.

This paper draws on a late medieval example to show that images of nature can also be images of the human body. It presents the Anthropologium de hominis dignitate by the Leipzig magister Magnus Hundt (1449–1519). The Anthropologium is a text that prominently integrates the human body into its conception of man and its account of human dignity. The body is not presented as a prison of the soul, but as a perfectly balanced physical counterpart to the soul. The paper shows how Hundt’s reflections were influenced by his commitment to the Thomistic school. Moreover, it reveals how the elevated Imago Dei thesis provides a justification for the study of the human body. Hundt is shown to offer nothing less than a theological–philosophical legitimation for practising medicine. In doing so, he also incorporates images of nature in a literal sense, insofar as he includes images of the human body in his book.

The issue of achieving controlled or targeted release of bioactive compounds with specific functional properties is a complex task that requires addressing several factors, including the type of bioactive, the nature of the delivery system, and the environmental conditions during transportation and storage. This paper deals with extensive reporting for the identification of original articles using Scopus and Google Scholar based on active packaging as a novel packaging technology that controls the release of antimicrobial agents encapsulated into carriers in the food packaging systems. For evidence-based search, the studies were extracted from 2015 to 2020 and screened using the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Following the review and screening of publications, 32 peer-reviewed articles were subjected to systematic analysis. The preliminary search indicated that the encapsulation of bioactives enhances their bioavailability and stability. From a theoretical viewpoint, mathematical models play an important role in understanding and predicting the release behavior of bioactives during transportation and storage, thus facilitating the development of new packaging material by a systematic approach. However, only a few studies could formulate parameters for mathematical models in order to achieve the specific release mechanism regulated for the quality and safety of foods. Therefore, this paper will cover all encapsulation approaches, active packaging, and mathematical modeling in the food industry into structural form and analyze the challenges faced by the complex nature of active packaging in real food systems.

The ramp-up of electromobility requires cross-industry holistic solutions. However, bringing together stakeholders from different branches holds challenges. One prerequisite for successful collaboration is a uniform understanding of roles, processes, and interfaces. Based on existing methods and experience from former projects, this paper describes a method for the systematic description of use cases for smart charging of electric vehicles. This method enables a uniform understanding of all actors involved and guarantees application-oriented usability. The unIT-e2 use case methodology consists of the business-use case level and the technical-use case level, which describe the use case in a structured layout. The method was applied in all so-called clusters of project unIT-e2. In total, we identify 25 higher-level business use cases and highlight similarities and differences between them. Further, this paper describes the business-use case regulatory-defined grid-serving flexibility in detail.

This paper will give an overview about the development of a material system, which consists of a novel formable magnesium alloy that is provided with an effective corrosion protection coating. The corrosion protection is to be realized in the form of a forming stable powder coating, which can be applied in the coil coating process. To improve the tribological properties, additional additives are added to the powder coating. Within this paper, first results concerning tribological and forming behavior of the new lightweight material system are given. For the benchmark, the results are compared with commercially available AZ31 Mg alloy.

Transitioning to Affordable and Clean Energy is a collective volume which combines original contributions and review papers that address the question how the transition to clean and affordable energy can be governed. It will cover both general analyses of the governance of transition, including policy instruments, comparative studies of countries or policies, and papers setting out scientifically sound visions of a clean and just energy system. In particular, the following aspects are foregrounded: - Governing the supply and demand side transformation - Geographical and cultural differences and their consequences for the governance of energy transitions - Sustainability and justice related to energy transitions (e.g., approaches for addressing energy poverty) Transitioning to Affordable and Clean Energy is part of MDPI's new Open Access book series Transitioning to Sustainability . With this series, MDPI pursues environmentally and socially relevant research which contributes to efforts toward a sustainable world. Transitioning to Sustainability aims to add to the conversation about regional and global sustainable development according to the 17 SDGs. The book series is intended to reach beyond disciplinary, even academic boundaries.

This article was presented at the 4th International Workshop on Morphological Computing (MORCOM 2021), IS4SI Summit 2021, online, 12–19 September 2021. This paper builds upon a recent proposal of a pragmatically-oriented framework for the practice of ‘Complex Thinking’ to further conceptualize it as a mode of thinking bridging different modes of thinking and types of cognitive activity. It explores the application of this framework for the design of human and computer-guided tools and strategies, or ‘Other’ Intelligences (OI), including AIs, which, coupled with a given observer/intervenor, would support and augment the enactment of Complex Thinking. This paper raises questions regarding the morphological constraints, challenges, and implications associated with the design of systems of (Co)Augmented Intelligences informed by Complex Thinking which aim to promote it.

For microbial enhanced oil recovery (MEOR), different mechanisms have been introduced. In some of these papers, the phenomena and mechanisms related to biosurfactants produced by certain microorganisms were discussed, while others studied the direct impacts of the properties of microorganisms on the related mechanisms. However, there are only very few papers dealing with the direct impacts of microorganisms on interfacial properties. In the present work, the interfacial properties of three bacteria MJ02 (Bacillus Subtilis type), MJ03 (Pseudomonas Aeruginosa type), and RAG1 (Acinetobacter Calcoaceticus type) with the hydrophobicity factors 2, 34, and 79% were studied, along with their direct impact on the water/heptane interfacial tension (IFT), dilational interfacial visco-elasticity, and emulsion stability. A relationship between the adsorption dynamics and IFT reduction with the hydrophobicity of the bacteria cells is found. The cells with highest hydrophobicity (79%) exhibit a very fast dynamic of adsorption and lead to relatively large interfacial elasticity values at short adsorption time. The maximum elasticity values (at the studied frequencies) are observed for bacteria cells with the intermediate hydrophobicity factor (34%), however, at longer adsorption times. The emulsification studies show that among the three bacteria, just RAG1 provides a good capability to stabilize crude oil in brine emulsions, which correlates with the observed fast dynamics of adsorption and high elasticity values at short times. The salinity of the aqueous phase is also discussed as an important factor for the emulsion formation and stabilization.

This paper provides the basis for understanding the preparation and properties of an old, but advanced material: activated carbon. The activated carbons discussed herein are obtained from “green” precursors: biomass residues. Accordingly, the present study starts analyzing the components of biomass residues, such as cellulose, hemicellulose, and lignin, and the features that make them suitable raw materials for preparing activated carbons. The physicochemical transformations of these components during their heat treatment that lead to the development of a carbonized material, a biochar, are also considered. The influence of the chemical activation experimental conditions on the yield and porosity development of the final activated carbons are revised as well, and compared with those for physical activation, highlighting the physicochemical interactions between the activating agents and the lignocellulosic components. This review incorporates a comprehensive discussion about the surface chemistry that can be developed as a result of chemical activation and compiles some results related to the mechanical properties and conformation of activated carbons, scarcely analyzed in most published papers. Finally, economic, and environmental issues involved in the large-scale preparation of activated carbons by chemical activation of lignocellulosic precursors are commented on as well. This research was funded by Generalitat Valenciana (PROMETEO/2018/076), European Commission/FEDER, and the University of Alicante (VIGROB-136).

Previous research demonstrated that the cancer diagnosis and treatment evoke existential concerns, especially ones related to meaning in life and meaning-making processes. The cancer experience is a vital challenge that often entails changes in what is personally important in life. Firstly, this paper collects evidence supporting that meaning adaptability, the way people adapt their meaning in life to the cancer experience, is a central element in the mental health of cancer patients. Various theories that could account for this meaning adaptability are introduced. Secondly, the paper provides a compilation of studies showing how people change what is significant in their lives within the course of cancer. Finally, the paper presents the available psychological therapies directed to facilitate meaning adaptability in this population. Meaning-centered interventions have been demonstrated to be effective in alleviating the suffering related to the cancer experience and promoting personal growth.

This paper presents and analyses solutions where open education and open science were utilised to reduce the impact of the COVID-19 pandemic on education. The COVID-19 outbreak and associated lockdowns created huge challenges in school and higher education, demanding sudden responses which aimed to sustain pedagogical quality. Responses have varied from conservative to radically innovative. Universally, the COVID-19 pandemic disrupted and shocked societies worldwide, and education systems were on the front line. The lockdowns largely stopped face-to-face and formal education in almost all countries, and in most cases, distance learning soon became the ‘new normal’. A central challenge concerned sustaining educational visions and ideals in such circumstances. To better understand the state of the art in the educational landscape, we collected case studies from 13 countries during the first year of the pandemic starting on 11 March 2020 (when the World Health Organization declared a pandemic). This paper presents summaries of the full country reports that were collected and describe lessons learned. Our overall aim was to identify good practices and recommendations from the collected case studies that can be taken forward in the future. We categorised the responses on the three generic educational levels (macro, meso and micro) and identified seven key aspects and trends that are valid for all or most countries: (1) formal education at a distance for first time; (2) similar approaches for formal education; (3) missing infrastructure and sharing open educational resources; (4) diverse teaching and learning methods and practices; (5) open education and access to open educational resources; (6) urgent need for professional development and training for teachers and (7) assessing and monitoring learning environments, teachers and students. Finally, we identified key recommendations on how open education and open science can benefit formal education in schools and universities in the future, namely, improved awareness of open educational practices, provision of ICT infrastructure, embracing and sustaining the practice of open access publications and OERs, capacity building for stakeholders and finally encouraging research and development in the area of open education and open science. We found significant evidence for the proposition that open education and open science can support both traditional face-to-face and distance learning.

The complex nature of sustainability challenges implies the need to provide students with interdisciplinary learning experiences and environments based on active and reflective learning. To know whether these experiences result in real learning, there must be a way of capturing and measuring the competences required to promote sustainable development using suitable indicators. This paper presents the process of building a competence map that is used as a navigation chart to monitor the sustainable education competences in the Ocean i3 experience. An action-research methodological approach is used involving participant observation, field notes, informal interviews, and documentary analysis. The participants were 38 students, 23 teachers, 3 project coordinators, and 2 researchers, and the context of the study is the five workshops carried out in the Ocean i3 project. The result is a navigation chart that traces the students’ learning journey through dialogue between the competences, learning outcomes, and activities. In conclusion, this approach to curricular planning can serve to inspire other learning environments and experiences on how to tackle the challenge of envisioning their sustainability competence development pathway. Above all, it can serve to improve competence development training schedules for sustainability. The Ocean i3 project. Blue skills to develop the blue economy on the Basque-Aquitaine cross-border coast was jointly financed by the European Regional Development Fund (ERDF) through the Spain–France–Andorra cooperation programme (POCTEFA 2014-2020) set up by the European Union. The aim of the project is to strengthen skills and inclusion within the territories. The paper is set in this context and its publication has been financed through the IkasGura research group: educational change at the university (GIU20/040).

Activity recognition based on inertial sensors is an essential task in mobile and ubiquitous computing. To date, the best performing approaches in this task are based on deep learning models. Although the performance of the approaches has been increasingly improving, a number of issues still remain. Specifically, in this paper we focus on the issue of the dependence of today’s state-of-the-art approaches to complex ad hoc deep learning convolutional neural networks (CNNs), recurrent neural networks (RNNs), or a combination of both, which require specialized knowledge and considerable effort for their construction and optimal tuning. To address this issue, in this paper we propose an approach that automatically transforms the inertial sensors time-series data into images that represent in pixel form patterns found over time, allowing even a simple CNN to outperform complex ad hoc deep learning models that combine RNNs and CNNs for activity recognition. We conducted an extensive evaluation considering seven benchmark datasets that are among the most relevant in activity recognition. Our results demonstrate that our approach is able to outperform the state of the art in all cases, based on image representations that are generated through a process that is easy to implement, modify, and extend further, without the need of developing complex deep learning models.

This article belongs to the Special Issue Advances in Quantum Artificial Intelligence and Machine Learning [Abstract] In this paper, we illustrate that inaccurate knowledge can be efficiently implemented in a quantum environment. For this purpose, we analyse the correlation between certainty factors and quantum probability. We first explore the certainty factors approach for inexact reasoning from a classical point of view. Next, we introduce some basic aspects of quantum computing, and we pay special attention to quantum rule-based systems. In this context, a specific use case was built: an inferential network for testing the behaviour of the certainty factors approach in a quantum environment. After the design and execution of the experiments, the corresponding analysis of the obtained results was performed in three different scenarios: (1) inaccuracy in declarative knowledge, or imprecision, (2) inaccuracy in procedural knowledge, or uncertainty, and (3) inaccuracy in both declarative and procedural knowledge. This paper, as stated in the conclusions, is intended to pave the way for future quantum implementations of well-established methods for handling inaccurate knowledge. This work was supported by the European Union’s Horizon 2020 research and innovation programme under project NEASQC (grant agreement No. 951821) and by the Xunta de Galicia (grant ED431C 2018/34) with the European Union ERDF funds. We wish to acknowledge the support received from the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014–2020 Program, grant ED431G 2019/01) Xunta de Galicia; ED431C 2018/34 Xunta de Galicia; ED431G 2019/01

Hypnosis is a commonly used therapy option in dentistry and medicine for fear and pain reduction. Nevertheless, it is viewed very critically, as there is still insufficient evidence for a treatment effect. Specific phobia of dental treatment and dental anxiety are prevalent conditions that can cause an oral health impairment. This paper critically reviews 19 clinical trials aimed at reducing dental anxiety and fear avoidance in adults, published in peer-reviewed journals between 1979 and 2021. The search identified 257 papers; 223 were selected after removing duplicates. A total of 188 articles were excluded after title and abstract evaluation; 35 full text articles were assessed for eligibility. Another 10 papers were discharged after full text evaluation, as these were case reports and questionnaires. Six papers were discharged due to the lack of a comparable scale to measure dental anxiety. The following treatment techniques were reviewed: various forms of cognitive-behavioral therapy (CBT), relaxation training, benzodiazepine premedication, self-hypnosis by audio therapy, hypnotherapy, hypnosis, and nitrous oxide sedation. CBT delivered in a variety of formats, including one-session treatment, showed the most evidence for the efficacy of reducing anxiety. A wide heterogeneity of methods allowed only the inclusion of five studies to the performed meta-analysis, showing contrasting results for the application of hypnosis. The main reason for this issue is the great variety in methods used, making a distinct assessment of hypnotic interventions difficult. However, the results of the systematic review are promising in that hypnosis can also be regarded as powerful and successful method for anxiety reduction, while there are also studies with a small or even slightly negative effect. Therefore, further research is needed. Within the limitations of the current study, a more consistent use of methods to examine anxiety for hypnosis research is recommended.

Once the first initial ripples have developed, they form according to the actual flow forces and sediment properties. In this paper, a semianalytical approach to determine the length of the developed ripples is presented. The theory assumes initial disturbances at the bed surface and corresponding flow separations resulting from an individual respective boundary layer. What causes the initial rhythmic perturbations is not the subject of this paper. Based on boundary layer theory, this approach explains a possible physical background for the existence and length of developed ripples in cohesion-free sediments. At the same time, the approach provides a distinction from dunes: ripples are sand waves affected by a viscous sublayer, and dunes are sand waves where this is not the case. Applications to Earth, Mars, and Titan are shown.

Employing electromagnetic fields (EMFs) in new wireless communication and sensing technologies has substantially increased the level of human exposure to EMF waves. This paper presents a useful insight into the interaction of electromagnetic fields with biological media that is defined by the heat generation due to induced currents and dielectric loss. The specific absorption rate (SAR) defines the heating amount in a biological medium that is irradiated by an electromagnetic field value. The paper reviews the radio frequency hazards due to the SAR based on various safety standards and organisations, including a detailed investigation of previously published work in terms of modelling and measurements. It also summarises the most common techniques utilised between 1978 and 2021, in terms of the operational frequency spectrum, bandwidth, and SAR values. The European Union’s Horizon 2020 innovation programme under grant agreement H2020- MSCA-ITN-2016 SECRET-722424 and the U.K. Engineering and Physical Sciences Research Council (EPSRC) under grant EP/E022936/1 supported this research. This study is also sponsored by the FCT/MEC through national funds and, where applicable, co-financed by the ERDF under the PT2020 Partnership Agreement under the UID/EEA/50008/2020 project. This study is part of the POSITIONII project, which is funded by the European Commission’s Joint Undertaking under the grant number Ecsel-7831132-Postitio-II-2017-IA. Moreover, this paper is also partially funded by British Council “2019 UK-China-BRI Countries Partnership Initiative” programme, with project titled “Adapting to Industry 4.0 oriented International Education and Research Collaboration”. In addition, this project has received funding from Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant 801538.

This paper addresses an experimental approach to the archaeological study of Roman camps in NW Iberia using ground-penetrating radar (henceforth GPR). The main goal is to explore the capabilities of GPR to extract datasets from ephemeral features, such as temporary camps or siege works, among others. This information aims to maximise the data available before excavation, orienting it to areas that could provide good results in terms of feature detection and contrast between soil matrix and archaeological deposits. This paper explores the potential of the GPR approach and volumetric data visualisation to improve our understanding of four ephemeral sites: Alto da Raia (Montalegre, Portugal–Calvos de Randín, Spain), Sueros de Cepeda (Villamejil, Spain), Los Andinales (Villsandino, Spain), and Villa María (Sasamón, Spain). Despite the focus of this paper, other survey techniques (namely LiDAR, aerial photography, and magnetometry) were used in combination with GPR. Further excavation of the sites provided ground truthing for all data remotely gathered., The Alto da Raia archaeological survey in 2021 was funded by the Montalegre and Calvos de Randín municipalities and done in collaboration with Era-Arqueologia (PIPA 201633). The survey in Sasamón and Villasandino has been funded by the Junta de Castilla y León (Contract 2021/00352/001) and Diputación de Burgos (Bopbur 5 de Agosto de 2021, P0937500G), together with the municipalities of Sasamón and Villasandino. Archaeological works at the Sueros de Cepeda camp were developed thanks to a contract established between the Villamejil City Council and the INCIPIT-CSIC (221294 CSIC ID contract, under the supervision of DGA). JGS is a postdoctoral researcher from the Instituto de Arqueología, Mérida-Junta de Extremadura, whose work is produced within an Atracción del Talento postdoctoral contract (Ref. TA18060). JMCG research was facilitated by a postdoctoral research contract funded by the Galician Autonomous Government in 2019 (Ref. ED481D 2019/006). JF research was funded by the European Commission through a Marie Skłodowska-Curie grant (agreement ID 794048). Part of JF research was also funded by FEDER through the COMPETE 2020 Programme, Lisboa Regional Programme and European Regional Development Fund (FEEI), and National Funds through FCT (Fundação para a Ciência e Tecnologia) under the scope of the Iberian Tin project (PTDC/HAR-ARQ/32290/2017). DGA research was facilitated by a 2019 Juan de la Cierva-Incorporación postdoctoral contract (Ref. IJC2019-040603-I) funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033).

Huntington Disease (HD) is a degenerative neurological disease that causes a significant impact on the quality of life of the patient and eventually death. In this paper we present an approach to create a biomarker using as an input DNA CpG methylation data to identify HD patients. DNA CpG methylation is a well-known epigenetic marker for disease state. Technological advances have made it possible to quickly analyze hundreds of thousands of CpGs. This large amount of information might introduce noise as potentially not all DNA CpG methylation levels will be related to the presence of the illness. In this paper, we were able to reduce the number of CpGs considered from hundreds of thousands to 237 using a non-linear approach. It will be shown that using only these 237 CpGs and non-linear techniques such as artificial neural networks makes it possible to accurately differentiate between control and HD patients. An underlying assumption in this paper is that there are no indications suggesting that the process is linear and therefore non-linear techniques, such as artificial neural networks, are a valid tool to analyze this complex disease. The proposed approach is able to accurately distinguish between control and HD patients using DNA CpG methylation data as an input and non-linear forecasting techniques. It should be noted that the dataset analyzed is relatively small. However, the results seem relatively consistent and the analysis can be repeated with larger data-sets as they become available.

The smartphone has become an integral part of the education landscape. While there has been significant smartphone research in education under the guise of m-learning, the unique role of the device suggests that m-learning may not be an appropriate characterization. The purpose of this paper is to review the use of m-learning as a primary descriptor for smartphone- and learning-related research. In support of this goal, the paper reviews the definitions associated with m-learning, smartphones, and related technologies from the perspective of educational research. In addition, a review of author keywords of research on smartphones in education is used to provide context to the classification of the research. Finally, three theoretically guided smartphone programs are presented as evidence of the unique nature of smartphone and learning research. This review concludes with recommendations for the characterization of future research.

Decisions on transport policy measures have long-term and important impacts on the economy, environment and society. Transport policy measures can lock up capital for decades and cause manifold external effects. In order to allow policymakers to evaluate transport policies, the developed decision support tool facilitates the evaluation of the multidimensional impacts of the implementation of transport policies. The objective of the decision support toolset presented in this paper is to support transportation planning and design practices based on an integrated transportation analysis of the area of examination to determine the most applicable combination of mobility services. This paper provides a comprehensive description of the interactive decision support tool implemented to help cities and decision makers design their strategies and shape the urban mobility of the future.

The shift toward electric mobility in Germany is a major component of the German climate protection program. In this context, public charging is growing in importance, especially in high-density urban areas, which causes an additional load on the distribution grid. In order to evaluate this impact and prevent possible overloads, realistic models are required. Methods for implementing such models and their application in the context of grid load are research topics that are only minorly addressed in the literature. This paper aims to demonstrate the entire process chain from the selection of a modelling method to the implementation and application of the model within a case study. Applying a stochastic approach, charging points are modelled via probabilities to determine the start of charging, plug-in duration, and charged energy. Subsequently, load profiles are calculated, integrated into an energy system model and applied in order to analyze the effects of a high density of public charging points on the urban distribution grid. The case study highlights a possible application of the implemented probabilistic load profile model, but also reveals its limitations. The primary results of this paper are the identification and evaluation of relevant criteria for modelling the load profiles of public charging points as well as the demonstration of the model and its comparison to real charging processes. By publishing the determined probabilities and the model for calculating the charging load profiles, a comprehensive tool is provided.

This paper presents a discussion on Quebec’s pegmatite lithium resources and potential markets. It also evaluates the opportunities of lithium battery production for electric vehicles (EV) in the province while reducing greenhouse gas emissions. The paper shows that mining Quebec’s lithium ore deposits would be sufficient to satisfy the province’s lithium demand and also for exporting abroad lithium-ion batteries. By considering only the projects whose final product is LMH or LCE, Quebec would be able to produce between 10 and 21 million lithium-ion batteries for electric vehicles with a greenhouse gas emissions footprint of only 43% of the international average value due to Quebec’s hydro power. Finally, considering Quebec’s lithium mining project economics, the increased future demand for lithium would render Quebec’s lithium pegmatite projects competitive compared with those reported for brine projects.

In this paper, the traceability of heparin medicine will be studied. Currently, the registration of traceability data is conducted in a decentralized manner. With blockchain implementation, the traceability systems that use this data could become semi-automated, increasing the quality, security, and confidence of the information generated in the supply chain. This paper presents the essential requirements and activities wherein information must be collected within the heparin drug supply chain, focusing on the animal raw materials production link and its requirements. Blockchain technology is proposed to increase traceability and reliability in relation to the current situation. It also fulfills all the requirements identified if used as part of a traceability system. These requirements are: the existence of a consensus mechanism; anonymity; protocol, efficiency, and consumption; immutability; ownership and management; and approval time. We conclude the paper by presenting the mapping of requirements and entities and critical activities for adopting blockchain technology to support the traceability of raw materials from animals used in heparin production.

The paper addresses through the new type concept the analysis of the feasibility of agricultural systems adaptable to environmental requirements. The development of a conceptual system for the feasibility of soil techniques through the responsible management of agricultural technological mechanisms can be expected to mitigate the impact of climate change. This paper analyzes the role of agricultural technologies based on the implementation of sustainable agriculture; as well as their interconnectivity in the local environment. In the methodological analysis was performed by compiling statistical data on agricultural production and effects of greenhouse gases (GHG) on agriculture

Machine learning is constantly gaining ground in the mining industry. Machine learning-based systems take advantage of the computing power of personal, embedded and cloud systems of today to rapidly build models of real processes, something that would have been impossible or extremely time-consuming a couple of decades ago. The widespread access to the internet and the availability of cheap and powerful cloud computing systems led to the development and acceptance of tools to automate resource modelling processes or optimise mine scheduling, using machine learning methodologies. The domain modelling system discussed in this paper, called DomainMCF, has been developed by Maptek, using artificial neural network technology. In the application presented in this paper, DomainMCF is used to model the spatial distribution of marble quality categorical parameters, and the results are combined to produce a final marble quality classification using drillhole and quarry face samples from an operational marble quarry in NE Greece. DomainMCF was made available for this study as a cloud processing service through an early access program for individuals or companies interested in testing its capabilities and suitability in various modelling scenarios and geological settings. The resulting marble product classifications are compared with those produced by the already established classification system that is based on a more conventional estimation method. The produced results show that DomainMCF can be effectively applied to the modelling of marble quality spatial distribution and similar domaining problems.

Greece is gifted with geologic features that promote geothermal heat flow. Geothermal energy exploration began in the late 60s, culminating in the first geothermal energy law in 1984 and the introduction of geothermal energy as a mineral resource under the amendment of the Greek Mining Code. Since then, low- and high-temperature geothermal activities followed their separate ways, with a modest utilization of the energy product in the primary sector (agriculture, aquaculture) and attempts for electricity production stalled since the mid-1990s. The adoption of green policies by both the EU and Greece, the acceptance of global warming as an existing threat, the adhesion to CO2 reduction goals, energy efficiency and the application on renewable energy solutions as means to combat the increase in global temperature have led to an increasing interest in the utilization of the geothermal energy applications. This paper presents the new legal framework for geothermal energy established by Law 4602/2019, as introduced by the Greek Ministry of Environment and Energy, Directorate-General for Mineral Raw Materials and discusses its scope and goals set by the implementation of its provisions. The paper offers a roadmap to successfully test those new policies and regulatory provisions and, finally, it maps the interfaces of stakeholders and geothermal industry in an attempt to highlight the steps of the necessary administrative procedures towards the facilitation of viable geothermal projects.

open access article The traditional and indigenous architecture in Saudi Arabia is being replaced by modern, Western-style buildings, resulting from the growing influence of Western culture. This change is evident in architectural elements such as Mashrabiya, which was once a symbol of the country’s Islamic architecture. The research paper aims to analyse the impact of modernization and the resurgence of Mashrabiya on a building’s energy efficiency, as well as the cultural and religious integrity of its historical counterpart in the context of Saudi Arabia. The study employed mixed research methods, including an online survey with Saudi Arabian households to study the public perception of Mashrabiya, case studies of residences with traditional and modern Mashrabiya, and a range of dynamic thermal simulations on a residence with traditional Mashrabiya to study its impact on energy consumption. The survey revealed that the public is aware of the changing face of Mashrabiya and its impact on the cultural and religious identity of the region, mainly privacy, but it is largely uninformed about ways to preserve its true essence. The simulations demonstrated a significant improvement in thermal comfort, i.e., an approximate 14% reduction in operative temperature and a 77.8% reduction in peak solar gain; an improvement in energy efficiency, i.e., a 5.7% reduction in monthly cooling load and a 35.5% improvement in daylight factor. Based on the findings, the research provides several recommendations to preserve the identity of traditional Mashrabiya while enhancing their energy efficiency, such as the incorporation of traditional design elements into the plans of the Saudi Arabian government.

This paper investigates the possibility of using a nanolaminate TiO2/Ag/TiO2 structure as a transparent conductive coating on GaAs solar cells. A novel result is that this structure forms an Ohmic contact to Al-rich AlGaAs, which is used as a “window” layer in GaAs-based solar cells. The TiO2/Ag/TiO2 structure is deposited by RF magnetron sputtering at room temperature. This nanolaminate coating has good optical and electrical properties: a high transmittance of 94% at 550 nm, a sheet resistance of 7 Ω/sq, and a figure of merit (FOM) of 105 × 10−3 Ω−1. These properties are the result of the presence of a discontinuous layer of Ag between two thin layers of TiO2. The morphology of a discontinuous layer of Ag nanogranules is confirmed by the observation of a cross-section of a sample with high-resolution transmission electron microscopy (HRTEM) and EDX analyses. Current–voltage diode characteristics of GaAs solar cells measured under standard test illumination at 1000 W/m2 are analyzed. The formation of an Ohmic contact is explained by the Fermi-level pinning effect caused by nanosized Ag particles in the nanolaminate TiO2/Ag/TiO2 structure. The obtained results demonstrate a new application of oxide−metal−oxide (OMO) coatings as Ohmic contacts to III-V compound semiconductors.

Advances in cosmology and astronomical observations over the last two decades have revealed significant tensions and many ambiguities within the standard model of cosmology of a spatially flat Universe, the lambda cold dark matter model. Moreover, the recent Planck Legacy 2018 (PL18) release has confirmed the presence of an enhanced lensing amplitude in the cosmic microwave background (CMB) power spectra, which prefers a positively curved early Universe with a confidence level higher than 99%. This paper addresses the study of a quantum mechanism that could replace the concept of dark matter and energy by considering a primordial curvature as preferred by the PL18 release while yielding the present-day spatial flatness. The implied primordial curvature is incorporated as the background curvature to extend the field equations in terms of the brane-world modified gravity. The Universe evolution is modelled by utilizing a new wavefunction of the Universe that propagates in the bulk with reference to the scale factor of the early Universe and its radius of curvature upon the emission of the CMB, which revealed both positive and negative solutions. This characteristic implies that a pair of entangled wavefunctions was created and evolved in opposite directions as a manifestation of distinct matter and antimatter sides of the Universe. The wavefunction indicates a nascent hyperbolic expansion away from early energy is followed by a first phase of decelerating expansion during the first 10 Gyr, and then, a second phase of accelerating expansion in reverse directions, whereby both sides free-fall towards each other under gravitational acceleration. The predicted background curvature evolution demonstrates the fast orbital speed of outer stars owing to external fields exerted on galaxies as they travelled through earlier conformally curved spacetime. Finally, the wavefunction predicts an eventual phase of rapid spatial contraction that culminates in a Big Crunch, signaling a cyclic Universe. These findings reveal that early plasma could be separated and evolved into distinct sides of the Universe that collectively and geometrically inducing its evolution, physically explaining the effects attributed to dark matter and energy.

This paper presents a first investigation into the aerodynamics and the performance breakdown of a distributed aft-fuselage Boundary Layer Ingesting (BLI) tube-and-wing aircraft using fully coupled unsteady RANS calculations that resolve the complete fan and installation geometries. Through the URANS simulations, the interaction between the turbulence from the fuselage Boundary Layer (BL) and the BLI propulsor is identified as an area that warrants further research. Using the URANS approach, the ingested turbulence leads to a 4.5% reduction in propulsor stage total-total isentropic efficiency. A mechanical power balance has been drawn up to compare the power sources and sinks throughout the installation and propulsor for two test cases with different thicknesses of ingested BL. The test case with a thinner BL was found to generate significantly more dissipation in the BL development upstream of the propulsor, the flow separation over the outer cowl, and the interaction between reversed flow over the cowl and the propulsor exhaust jet. Due to this increase in dissipation, the case with thinner ingested BL consumes 7% more power relative to a baseline case with thicker BL, representative of the upstream fuselage at cruise. This demonstrates the importance of matching the installation with the incoming fuselage BL., This research was funded by Rolls-Royce plc and EPSRC under the Centre for Doctoral 503 Training in Gas Turbine Aerodynamics (EP/L015943/1) with a reference number 1799072.

This paper discusses the analysis and computations of chaos–hyperchaos (or vice versa) transition in Rössler–Nikolov–Clodong O (RNC-O) hyperchaotic system. Our work is motivated by our previous analysis of hyperchaotic transitional regimes of RNC-O system and the results recently obtained from another researchers. The analysis and numerical simulations show that chaos–hyperchaos transition in RNC-O system is coupled to change in the equilibria type as one large hyperchaotic attractor occurs. Moreover, we show that for this system, a zero-Hopf bifurcation is not possible. We also consider the cases when the divergence of the system is a constant and detected two families of exact solutions.

Hydrophobicity is an important physicochemical property of peptides in solution. As well as being strongly associated with peptide stability and aggregation, hydrophobicity governs the solution based chromatographic separation processes, specifically reversed-phase liquid chromatography (RPLC). In addition, hydrophobicity is a major physicochemical property of peptides in comparison to H-bonding, electrostatic, and aromatic properties in intermolecular interactions. However, a wide range of molecular factors can influence peptide hydrophobicity, with accurate predictions depending on specific peptide amino acid compositions, structure, and conformation. It is noticeable that peptide composition, the position of the amino acid, and its neighbouring groups play a crucial role in the elution process. In light of this, the same amino acid behaved differently depending on its position and neighbouring amino acid in the peptide chain. Extra attention should be paid to the denaturation process during the course of elution, as it has been shown to complicate and alter the elution pattern. This paper reports on the key peptide properties that can alter hydrophobicity and, consequently, the RPLC elution behaviour of the peptides, and it will conclude by proposing improved prediction algorithms for peptide elution in RPLC.

Knowledge Graphs (KGs), a structural way to model human knowledge, have been a critical component of many artificial intelligence applications. Many KG-based tasks are built using knowledge representation learning, which embeds KG entities and relations into a low-dimensional semantic space. However, the quality of representation learning is often limited by the heterogeneity and sparsity of real-world KGs. Multi-KG representation learning, which utilizes KGs from different sources collaboratively, presents one promising solution. In this paper, we propose a simple, but effective iterative method that post-processes pre-trained knowledge graph embedding (IPPT4KRL) on individual KGs to maximize the knowledge transfer from another KG when a small portion of alignment information is introduced. Specifically, additional triples are iteratively included in the post-processing based on their adjacencies to the cross-KG alignments to refine the pre-trained embedding space of individual KGs. We also provide the benchmarking results of existing multi-KG representation learning methods on several generated and well-known datasets. The empirical results of the link prediction task on these datasets show that the proposed IPPT4KRL method achieved comparable and even superior results when compared against more complex methods in multi-KG representation learning.

This paper discusses what is, to our knowledge, the oldest subdaily measurement series of humidity taken over several years. Louis Morin performed the measurements in Paris, three times a day, between May 1701 and June 1711. A correlation analysis of Morin’s humidity measurements with various meteorological variables yields results comparable to those of a parallel analysis of the relative humidity measurements of the E-OBS data: the Spearman correlation coefficient between the humidity and the daily minimum temperature is -0.43 (p < 0.01); with the mean temperature, it is -0.54 (p < 0.01); with the maximum temperature, it is -0.59 (p < 0.01); with the diurnal temperature range, it is -0.65 (p < 0.01); and with the total cloud cover, 0.33 (p < 0.01). However, with a Spearman correlation coefficient of 0.11 (p < 0.01), no correlation is found with the precipitation data. Further evidence for the plausibility of the measurements is shown by a day-by-day analysis of the first half-year of 1709. Here, abrupt changes in the humidity measurements of Morin can be explained by the other measurements/observations of Morin. According to the correlation analysis, indirect notes in his journal, and others, we argue that Morin used the hygrometer developed by Vincenzo Viviani. However, the conversion of the data to common units is not performed and is subject to further research.