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Nucleation plays a critical role in the birth of crystals and is associated with a vast array of phenomena, such as protein crystallization and ice formation in clouds. Despite numerous experimental and theoretical studies, many aspects of the nucleation process, such as the polymorph selection mechanism in the early stages, are far from being understood. Here, we show that the hitherto unexplained excess of particles in a face-centered-cubic (fcc)-like environment, as compared to those in a hexagonal-close-packed (hcp)-like environment, in a crystal nucleus of hard spheres can be explained by the higher order structure in the fluid phase. We show using both simulations and experiments that in the metastable fluid phase, pentagonal bipyramids, clusters with fivefold symmetry known to be inhibitors of crystal nucleation, transform into a different cluster, Siamese dodecahedra. These clusters are closely similar to an fcc subunit, which explains the higher propensity to grow fcc than hcp in hard spheres. We show that our crystallization and polymorph selection mechanism is generic for crystal nucleation from a dense, strongly correlated fluid phase.

Henriques, R., & Pinto, L. (2023). A novel evaluation framework for recommender systems in big data environments. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2023.120659---We gratefully acknowledge the support of Aptoide in providing access to the data which made this project possible. This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Recommender systems were first introduced to solve information overload problems in enterprises. Over the last few decades, recommender systems have found applications in several major websites related to e-commerce, music and video streaming, travel and movie sites, social media, and mobile app stores. Several methods have been proposed over the years to build recommender systems. However, very little work has been done in recommender system evaluation metrics. The most common approach to measuring recommender system’s performance in offline settings is to employ micro or macro averaged versions of standard machine-learning measures. Profit or other business-oriented metrics have been proposed for other predictive analytics problems, such as churn prediction. However, no such metrics have emerged for the recommender system context. In this work, we propose a novel evaluation metric that incorporates information from the online-platform userbase’s behavior. This metric’s rationale is that the recommender system ought to improve customers’ repeatead use of an online platform beyond the baseline level (i.e. in the absence of a recommender system). An empirical application of this novel metric is also presented in a real-world mobile app store, which integrates the dynamics of large-scale big data environments, which are common deployment scenarios for these types of recommender systems. The resulting profit metric is shown to correlate with the existing metrics while also being capable of integrating cost information, thereby providing an additional business benefit context, which allows us to differentiate between two similarly performing models. publishersversion epub_ahead_of_print

International audience; In recent decades, most of construction activities have been taking place in developing countries such as the Middle East and North Africa region. The expansion in infrastructure has great impact in the technological, social, economic and environmental transformation of this region. Construction sector contribution to Gross Domestic Product (GDP) varies throughout the region and ranges between 2-10%. Currently, sustainable construction requires integrated and comprehensive sustainable design including careful choice of materials and methods. Materials that are locally available and require less energy to produce and transport would pave the way to more sustainable practices. Many countries in the region have realised the benefits of using local building materials on the economy, society and environment. This paper outlines the key trends and opportunities of using sustainable and affordable local building materials in the region to respond to the global climate change crisis and to promote more sustainable and environmentally friendly practices. The current practice of using various building materials such as bio-based materials, treated municipal solid waste incineration bottom ash (MSWI-BA), construction and demolition waste, gypsum-containing by-products (phosphorgypsum, FGD gypsum, borogypsum and others) and rammed earth will be reviewed. It is concluded that the use of these local building materials in construction activities would foster the development of the society. However, some of these materials are already developed and reached the implementation stage while most of them are still at R&D stage. Therefore, there is an urgent need for a comprehensive local and regional strategies to enhance the utilization of these materials.

Scott, I., Neto, M. D. C., & Pinheiro, F. L. (2023). Bringing trust and transparency to the opaque world of waste management with blockchain: a Polkadot parathread application. Computers & Industrial Engineering, 182(August), 1-17. [109347]. https://doi.org/10.2139/ssrn.3825072, https://doi.org/10.1016/j.cie.2023.109347---The authors acknowledge the financial support provided by Fundação para a Ciência e a Tecnologia (FCT) Portugal under the project: UIDB/0415s2/2020 - Centro de Investigação em Gestão de Informação (MagIC). The Project BEE2WasteCrypto (reference IDT-COP 45933) leading to this work is co-financed by the ERDF – European Regional Development Fund through the Operational Program for Competitiveness and Internationalisation COMPETE 2020, the Lisbon Portugal Regional Operational Program – LISBOA 2020 and by the Portuguese Foundation for Science and Technology – FCT under CMU Portugal Program. The majority of countries are currently struggling with unsustainable levels of waste production and low levels of recycling, particularly relating to household waste, and this area is in urgent need of new solutions. In general, the waste management sector has struggled with low consumer trust, fraud, manipulation, significant manual processes, and low levels of information and control. Recent events relating to the COVID-19 pandemic have highlighted, in particular, the role of trust in effective public policy making and consumer behavioural change. Here we propose a hybrid blockchain solution called a Polkadot parachain. Polkadot is a blockchain technology that connects a network of blockchains, each called a parachain, that can be customised to the business needs of a given application. This solution provides the cost benefits, scalability, and control of a permissioned or private blockchain while providing the security, verifiability, and trust of a public blockchain. The solution is developed with a design science approach and combines three typically separate blockchain use cases: supply chain tracking, incentivisation through a payment system, and gamification to achieve a complete solution for waste management. We provide a detailed discussion on the design of this blockchain solution with the use of blockchain functionality assessed against the criteria and development approaches found in the literature. Finally, we demonstrate how such a blockchain can be implemented with the Substrate blockchain development framework. preprint publishersversion epub_ahead_of_print

Ear biting is a welfare challenge in commercial pig farming. Pigs sustain injuries at the bite site paving the way for bacterial infections. Early detection and management of this behavior are important to enhance animal health and welfare, increase productivity, and minimize inputs from medication. Pig management using physical observation is impractical because of the scale of modern pig production systems. The same applies to the manual analysis of videos captured from pigsty. Therefore, a method of automated detection is desirable. In this study, we introduce an automatic detection pipeline based on deep learning for the quantification of ear biting outbreaks. Two state-of-the-art detection networks, YOLOv4 and YOLOv7, were trained to localize the regions of ear biting. The detected regions were tracked over multiple video frames using DeepSORT and Centroid tracking algorithms. Tracking provided the association between detected instances in video frames, enabling the computation of the frequency and duration of occurrence. The frequency and duration of ear biting were expressed as the cumulative performance of each group of pigs. The pipeline was evaluated using two datasets from experimental and commercial farms with diverse management and monitoring settings. The detection networks achieved comparable average precision values of 98% & 97.5% and 85.6% & 80.9% on the respective datasets. The tracking algorithms produced 14% and 34% False-Alarm rates, respectively. The results show that automated detection and tracking of ear biting is possible. Subsequently, we applied our method to videos in which pigs were managed in a manner that was expected to affect the frequency of ear biting to different degrees. This method can be used as the basis of an early warning system for the detection of ear-biting in commercial farms.

Abdelaziz, A., Santos, V., & Dias, M. S. (2023). Convolutional Neural Network with Genetic Algorithm for Predicting Energy Consumption in Public Buildings. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3284470---This work has been supported by Portuguese funds through FCT - Fundação para a Ciência e Tecnologia, I.P., under the project FCT UIDB/04466/2020, and this work has been supported by Information Management Research Center (MagIC) - NOVA Information Management School. Due to their capacity to improve energy consumption performance, intelligent applications have recently assumed a pivotal position in the energy management of public buildings. Because of their irregular energy consumption patterns and the lack of design criteria for energy efficiency and sustainability solutions, keeping these buildings’ energy consumption under control is a significant issue. As a result, it is important to analyze public building energy consumption patterns and forecast future energy demands. Evidence like this highlights the need to identify and categorize energy use trends in commercial and institutional structures. This research aims to identify the most effective intelligent method for categorizing and forecasting the energy consumption levels of public buildings and, ultimately, to identify the scientific rules (If-Then rules) that will aid decision-makers in establishing the energy consumption level in each building. The goals of this research were accomplished by employing two intelligent models, the Elbow technique and the Davis and Boulden approach, to count the number of clusters of energy consumption patterns. It was determined what the clustering levels would be in each structure using K-means and a genetic algorithm. In this step, the genetic algorithm was utilized to find the best centroid points for each cluster, allowing the fitting model to function better. Determining which buildings use the most energy has been made easier thanks to the extraction of If-Then rules from cluster analysis. Convolutional neural networks (CNNs) and CNNs combined with a genetic algorithm were also employed as intelligent models for energy consumption forecasting. At this point, we utilized a genetic algorithm to fine-tune some of CNN’s settings. CNN with genetic algorithm outperforms on CNN model in terms of accuracy and standard error. Using a genetic algorithm, CNN achieves a 99.01% accuracy on the training dataset and a 97.74% accuracy on the validation dataset, with accuracy and an error of 0.02 and 0.09, respectively. CNN achieves a 98.03% accuracy and a 0.05 standard error on the training dataset and a 94.91% accuracy and a 0.26 standard error on the validation dataset. This research is useful for policymakers in the energy sector because it allows them to make informed decisions about the timing of energy supply and demand for public buildings. publishersversion published

Carbon nanofibers (CNFs) is a typical electrical loss material with strong permittivity that has been widely used in the field of electromagnetic shielding, attenuation, and reception. However, studies have demonstrated that the unstable anisotropic EM behavior exhibited by CNFs in the terahertz domain hinders its application in the tremendously high frequency domain. In this study, ZnO is used to perform surface modification of CNFs based on the sputtering method, which effectively eliminate the anisotropic EM behavior of CNFs at different irradiation angles in the sub-terahertz band. ZnO coated the surface of fiber wall comprehensively in the form of nano-scale. The results show that after the surface modification, the composite sample exhibits a stable isotropic EM response in the sub-terahertz band of 500-750 GHz. Also, in the frequency range of 551-563 GHz (bandwidth: 12 GHz), ZnO not only acts as a stabilizer, but also enhances the absorption performance of the original CNFs by 6%, which is up to 92%. In the frequency range of 660-690 GHz (bandwidth: 30 GHz), the composite sample and the pure CNFs sample have similar wave absorption performance with about 75%, the maximum difference is less than 5%, and the minimum is about 2.6%.

Within urban centres, underground spaces such as storage areas, transportation corridors, basement car parks, public facilities, retail & office as well as private spaces (e.g., residential basements), present priority risk areas during flood events with respect to timely evacuation. However, often the location, geometry and volume of these underground spaces is not well known. Furthermore, these underground spaces are commonly not considered in flood prediction models for urban centres. At the same time, communicating flood risks and enhancing emergency preparedness poses further challenges. Thus emergency planners need engaging communication tools to increase community resilience and preparedness to flooding events. Low-cost Mobile Laser Scanning (MLS) and Airborne Laser Scanning (ALS) technologies as well as other remote sensing applications and street view imagery data are becoming increasingly available and affordable, providing high-resolution data for the urban environment. The presented project focusses on gaining better knowledge of the location and internal geometry of underground spaces in urban centres by using low-cost MLS data. The presented project is developing efficient database approaches to handle the large data sets and to integrate the information into flood prediction models. These models provide refined information on urban flood levels and flooding rates into the underground spaces, which in turn may be used as part of risk communication tools using low-cost virtual reality applications which provide an immersive experience of flood risk with the aim to better engage communities in emergency preparedness. The comparison of rapid low-cost high-resolution terrestrial MLS with ALS data for an urban case study site in Belfast (Northern Ireland) highlights the advantages of the rapid low-cost MLS approaches and their use for the detection and surveying of urban underground spaces and the integration of these identified priority flood risks into urban flood models. Incorporating the identified underground spaces provides more realistic inundation scenarios and enhanced information with regard to accessible evacuation routes to inform flood risk mitigation plans.

Waste production, carbon dioxide atmospheric accumulation, and dependence on finite natural resources are expressions of the unsustainability of the current industrial networks that supply fuels, energy, and manufacturing products. In particular, circular manufacturing supply chains and carbon control networks are urgently needed. To model and design these and, in general, any material networks, we propose to generalize the approach used for traditional networks such as water and thermal power systems by using compartmental dynamical thermodynamics and graph theory. The key idea is that the thermodynamic compartments and their connections can be added, removed or modified as needed to achieve a circular material flow. The design methodology is explained and its application is illustrated through examples. In addition, we provide a physics-based definition of circularity and, by implementing a nonlinear compartmental control, we strengthen the connection between "Industry 4.0" and "Sustainability". The paper source code is publicly available., Submitted

Funding Information: This work was financially supported by Portuguese national funds through projects UIDB/50006/2020, UIDP/50006/2020, and LA/P/0008/2020 from the Fundação para a Ciência e a Tecnologia (FCT)/Ministério da Ciência, Tecnologia e Ensino Superior (MCTES), and within the scope of the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020, UIDP/50011/2020 and LA/P/0006/2020, financed by national funds through the FCT/MCTES (PIDDAC) and through the fellowship SFRH/BD/144562/2019. Publisher Copyright: © 2023 by the authors. Featured Application: This work provides fundamental information regarding the technical, environmental, and economic viability of granite sludge valorisation as a substitute for feldspar in a ceramic paste and as a substitute for fine–medium inert in structural concrete. The granite sludge (GS) produced during block sawing can be exploited as alternative raw material in ceramic and concrete industries. Based on the case study of a Portuguese granite processing plant, this work analysed, by experimental tests and Environmental and Cost Life Cycle analyses, the feasibility of GS valorisation as a substitute (i) for feldspar in a ceramic paste and (ii) fine–medium inert filler in structural concrete. The results demonstrated that both the valorisation pathways are more advantageous than GS landfilling. Due to granulometric, mineralogical composition and shrinkage, GS can substitute feldspar in sandstone tiles or tableware products, although its tinting effect can limit noble whitish ceramic applications. In structural concrete mixes, 5% w/w GS instead of fine inert filler reduces the compressive strength and increases the water:cement ratio. The GS generates lower environmental impacts as a substitute for inert filler than as a substitute for feldspar in most of the impact categories analysed, even though the latter valorisation pathway provides higher benefits in Climate Change and the Depletion of Fossil resources, Water, and Ozone. If no monetary value is recognised for GS valorisation by the market, the sustainability of GS life cycle cost decreases when compared to its landfilling. publishersversion published

How we can accelerate the diffusion of new clean energy technologies worldwide is a highly relevant topic for energy and climate policies, as well as industrial policies. We trace the time lag between the introduction and the diffusion of breakthroughs in solar photovoltaic technology and wind power technology. Our results show that both domestic knowledge base and organizational proximity to the country introducing breakthroughs, help latecomer countries catch up by actively innovating in these technologies on their own. Moreover, we find that there are more opportunities for latecomer countries with stronger domestic knowledge base to catch up in solar photovoltaic technology than wind power technology. The results of this paper provide systematic evidence of the technology-sensitive catching-up process in the clean energy technological paradigm.

Funding Information: This research activity has been carried out at Department of Civil Engineering and Architecture of University of Trieste, and financially supported in the framework of “ComBioDyn” Microgrants 2022 project. Publisher Copyright: © 2023 by the authors. For structural design purposes, human-induced loads on pedestrian systems can be described by several simplified (i.e., deterministic equivalent-force models) or more complex computational approaches. Among others, the Spring-Mass-Damper (SMD), Single Degree of Freedom (SDOF) model has been elaborated by several researchers to describe single pedestrians (or groups) in the form of equivalent body mass m, spring stiffness k and damping coefficient c. For all these literature SMD formulations, it is proved that the biodynamic features of walking pedestrians can be realistically reproduced, with high computational efficiency for vibration serviceability assessment of those pedestrian systems mostly sensitive to human-induced loads (i.e., with vibration frequency f1 < 8 Hz). Besides, the same SMD proposals are characterized by mostly different theoretical and experimental assumptions for calibration. On the practical side, strongly different SMD input parameters can thus be obtained for a given pedestrian. This paper focuses on a selection of literature on SMD models, especially on their dynamic effects on different structural floor systems. Four different floors are explored (F#1 and F#2 made of concrete, F#3 and F#4 of glass), with high- or low-frequency, and/or high- (>1/130th) or low- (1/4th) mass ratio, compared to the occupant. Normal walking scenarios with frequency in the range fp = 1.5–2 Hz are taken into account for a total of 100 dynamic simulations. The quantitative comparison of typical structural performance indicators for vibration serviceability assessment (i.e., acceleration peak, RMS, CREST) shows significant sensitivity to input SMD assumptions. Most importantly, the sensitivity of structural behaviours is observed for low-frequency systems, as expected, but also for low-mass structures, which (as in the case of glazed floor solutions) can be characterized by the use of lightweight modular units with relatively high vibration frequency. As such, major attention can be required for their vibrational analysis and assessment. publishersversion published

Funding Information: Inês A. Ferreira acknowledges the financial support from Fundação para a Ciência e Tecnologia (FCT) for funding PhD Grant-REF: SFRH/BD/145448/2019. Helena Carvalho acknowledges Fundação para a Ciência e Tecnologia (FCT) for its financial support through the project UIDB/00667/2020 (UNIDEMI). Carina Pimentel acknowledges Fundação para a Ciência e Tecnologia (FCT) within the R&D Units Project Scope UIDB/00319/2020. Publisher Copyright: © 2023 by the authors. The number of variants of the vehicle routing problem (VRP) has grown rapidly in the last decades. Among these, VRPs with time window constraints are among the most studied ones. However, the literature regarding VRPs that concerns the delivery and installation of products is scarce. The main aim of this study was to propose a heuristic approach for the route planning process of a company whose focus is on furniture delivery and assembly and, thus, contributing to the research around the Delivery and Installation Routing Problem. The case study method was used, and two scenarios were compared: the current scenario (showing the routes created by the company worker); and the future scenario (representing the routes created by the heuristic). Results show that the proposed heuristic approach provided a feasible solution to the problem, allowing it to affect customers and teams without compromising the teams’ competencies and respecting all constraints. publishersversion published

Modern environmental policies, carbon emission reduction targets, stimulus funding for economy recovery, end-use energy efficiency, objectives for higher reliability, and service quality in energy systems are a few of the factors driving forces behind the integration of advanced control and communication technologies into energy systems [...]

In this study, the active tectonics, paleoseismicity, and seismic hazards of the Doroud Fault are examined through high-resolution satellite image interpretations, field investigations, outcrop and trench excavations, and the dating of geochronology samples. The Doroud Fault (DF), one of the essential segments of the Main Recent Fault in the northern margin of the Zagros mountain range, has a historical and instrumental background of high seismicity. We present the first constraints from tectonic geomorphology and paleoseismology along the Doroud Fault near the capital city of Dorud. Detailed observations from satellite imagery, field investigations, real-time kinematic (RTK) measurements, paleoseismological trenching, the radiocarbon (C14), and optically stimulated luminescence (OSL) as ages allowed us to map the fault in detail, describe and characterize its kinematics, and document its recent activity and seismic behavior (cumulative displacements, paleoseismicity, and magnitude, as well as recurrence interval) relevant to the recent seismic activity of the Doroud Fault during the late Holocene as one of the most important seismogenic faults in Zagros. Modern alluvial terraces of gullies and loess accumulations are systematically deflected and/or offset with co-seismic rupture, landslides, and scarps, indicating that the Doroud Fault has been active in the late Quaternary and is characterized by dextral strike–slip movements with a normal component. In addition, our findings provide a comprehensive analysis of the fault displacement, the timing of paleoearthquakes, and the right-lateral slip rate of the Doroud Fault. The late Holocene slip rate of the Doroud Fault using the OSL dating the gully is as follows: the minimum and maximum horizontal slip rates are estimated to be 1.82 and 2.71 mm/yr, and vertical slip rates of 1.03 and 1.53 mm/yr are calculated for the past 4600 ± 900 years in the middle segment of the fault. This study focused on a paleoseismological trench within the archeological sites of Darbe-Astaneh. The central portion of the fault has historically hosted more than nine earthquakes in the last 66 ka years, according to the study’s findings. According to paleoseismology studies, the Doroud Fault has the seismic capability to cause earthquakes with a magnitude of more than 7.4 and a total slip rate of about 3.83 ± 0.1 m. The average recurrence interval for the identified paleoearthquakes is approximately 104 ± 7 years.

Herein, the aim was to develop an in-depth understanding of the kinetic behaviour of olive tree pruning residue (OTPR), an abundant agricultural waste, during pyrolysis. Thermal analysis at 1, 2, 4, 6 and 10 °C.min−1 was performed using TGA-thermogravimetric analysis, with the results subsequently used to determine the OTPR's kinetic thermal breakdown behaviour. Furthermore, advanced kinetics and technology solutions (AKTS) thermo-kinetic tool was applied to investigate the kinetic behaviour of OTPR and to generate kinetic predictions for various heating configurations. Friedman's method was the main approach used to evaluate the kinetic parameters. For comparison, other established kinetic modelling techniques, such as ASTM-E698 and Flynn-Wall-Ozawa (FWO) methods, were applied. The ASTM-E698 approach yielded an apparent activation energy (Ea) of 172.09 kJ.mol−1, whereas the FWO method yielded an Ea range from 38 to 172 kJ.mol−1. Finally, the differential iso-conversional approach yielded Ea values ranging between 85 and 191 kJ.mol−1. Kinetic predictions were then developed for isothermal, non-isothermal, and stepwise configurations using the kinetic parameters obtained via Friedman’s model. The forecasts shed light on optimising production throughput in a variety of reactor configurations.

In this paper, rapid reconstruction of a three-dimensional (3D) image of a scene for near-field microwave imaging using a dynamic metasurface antenna (DMA) is addressed. The imaging system consists of a fixed transmitting DMA and a single moving antenna as the receiver. In the proposed approach, after pre-processing the raw data and converting it into an applicable form for Fourier computations, the converted data are processed by the presented algorithm (all with fast Fourier computations, without the need for the heavy Stolt interpolation step). Furthermore, after extracting a closed-form expression for image reconstruction, a minimization problem is defined using the compressive-sensing (CS) theory, which allows a sparse pattern to be sampled instead of collecting full data, thereby significantly improving the data acquisition rate (according to the CS rate). The performance of the proposed approach is examined by computer simulations and analytical discussions.

Publisher Copyright: © 2013 IEEE. This work was supported in part by the European Union (EU) Project Advanced Manufacturing Solutions Tightly Aligned With Business Needs (AVANGARD), and in part by the European Union's Horizon 2020 Research and Innovation Program under Grant 869986. The scheduling applied to manufacturing represents a huge opportunity for companies to stand out in a world of fast and big changes. Having a reliable scheduling system will allow factories to deal with the significant demand for highly customized products. Although manufacturing scheduling has been deeply studied for decades, there is still a gap between academia and industry, namely because the lack of flexibility and homogeneity among scheduling solutions, which makes them very use case-oriented. Furthermore, the absence of standardization is also making it difficult to implement smart scheduling solutions in industrial scenarios. Thus, this work presents a set of requirements and design principles based on axiomatic design concept, to make the first steps to standardize the designing and development of manufacturing scheduling solutions in the context of Industry 4.0. At the end, is presented a scheduling generic framework targeting smart manufacturing and evaluated in a practical use case. publishersversion published

Power systems with a high share of renewables require additional ancillary services to operate safely and reliably. System operators are introducing schemes to attract investment in technology which will provide ancillary services. Battery storage can provide some of these services but investment in equipment is required. This study investigates the potential benefits of energy storage and tests the market arrangements to attract investment. The study uses a combination of numerical and system analyses to test the financial performance. A dynamic economic dispatch model was used to evaluate the system costs and emission levels. A unit commitment model was used to measure the reserve cost. Both models use real-time load data for a region in the Irish electricity market. The ancillary service revenue is modelled based on actual renewable levels for the Irish system. The frequency and rate of change of frequency response are evaluated by introducing a disturbance to the system model with and without energy storage. The results were used to test investment opportunities using established financial appraisal techniques.

Funding Information: The first author acknowledges the FCT—Fundação para Ciência e a Tecnologia—for the financial support through the Ph.D. scholarship PD/BD/127844/2016. This work was financially supported by: UID/ECI/04708/2019- CONSTRUCT—Instituto de I&D em Estruturas e Construções funded by national funds through the FCT/MCTES (PIDDAC). The APC was funded by open access funds from Oslo Metropolitan University (OsloMet). Publisher Copyright: © 2023 by the authors. Climate change will greatly influence the world at several levels and will have consequences on the interior climate of historic buildings and artifacts conservation. Buildings are responsible for a large slice of the overall emissions, which is due both to the greenhouse gases that are released during their construction as well as the activities that are performed therewithin. One way of counteracting this trend is to design more efficient retrofit buildings and predict their behavior using simulation software, which can thoroughly assess the performance of new buildings or the impact of each retrofit measure for existing buildings. In this study, a calibrated computational model of high thermal inertia building was used to assess the performance of passive retrofits in mitigating the effects of climate change concerning artifact decay mechanisms. In addition, a methodology that aims to reduce the amount of time spent to perform these studies is also presented, in which time savings reach up to 63%. publishersversion published

Castelo-Branco, I., Henriques, M. M. D. A., Cruz-Jesus, F., & Oliveira, T. (2023). Assessing the Industry 4.0 European divide through the country/industry dichotomy. Computers and Industrial Engineering, 176(February), 1-14. [108925]. https://doi.org/10.1016/j.cie.2022.108925 Industry 4.0 refers to the application of new technologies to production and supply chain processes under the fourth industrial revolution (4th IR) paradigm and has been studied mainly in manufacturing. The present study looks to understand how the 4th IR manifests itself in the EU countries and across different industries through an Industry 4.0 perspective. Following a five-step research protocol that used a multivariate approach, Industry 4.0 Infrastructure, Big Data Maturity and Industry 4.0 Applications were identified as characterizing elements of Industry 4.0. Cluster analysis showed five homogeneous profiles of Industry 4.0 implementation across different industries and European countries. Our findings unveil a strong Industry 4.0 divide across (and within) European countries and industries. Industry 4.0 is much more determined by the industry than by the country. Knowledge of Industry 4.0 development conditions would greatly benefit from further research on the elements that drive the Industry 4.0 divide at the industry and country levels. publishersversion published

Featured Application: The presented paper conducted a comparative analysis based on well-known MLP, SVM, DT, and RF learning methods to assess/predict the safety factor (F.S) of earthslopes. Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S) plays an essential part in the stability assessment, which guarantees operations’ success. Providing accurate and reliable F.S can be used to improve the stability analysis procedure as well as stabilizations. In this regard, researchers used computational intelligent methodologies to reach highly accurate F.S calculations. The presented study focused on the F.S estimation process and attempted to provide a comparative analysis based on computational intelligence and machine learning methods. In this regard, the well-known multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF) learning algorithms were used to predict/calculate F.S for the earth slopes. These machine learning classifiers have a strong capability predict the F.S under certain conditions for slope failures and uncertainties. These models were implemented on a dataset containing 100 earth slopes’ stabilities, recorded based on F.S from various locations in the provinces of Fars, Isfahan, and Tehran in Iran, which were randomly divided into the training and testing datasets. These predictive models were validated by Janbu’s limit equilibrium analysis method (LEM) and GeoStudio commercial software. Regarding the study’s results, MLP (accuracy = 0.901/precision = 0.90) provides more accurate results to predict the F.S than other classifiers, with good agreement with LEM results. The SVM algorithm follows MLP (accuracy = 0.873/precision = 0.85). Regarding the estimated loss function, MLP obtained a 0.29 average loss in the F.S prediction process, which is the lowest rate. The SVM, DT, and RF obtained 0.41, 0.62, and 0.45 losses, respectively. This article tried to fill the gap in traditional analysis procedures based on advanced procedures in slope stability assessments.

This work was supported by the Spanish Government, the Spanish National Research Council (CSIC). We also thank Dr Martinez from Novozymes. Publisher Copyright: © 2023 RSC. The effect of the temperature in the synthesis of Pd nanoparticles in the metal-enzyme biohybrids is evaluated. The effect on the formation, size, and morphology of nanoparticles was evaluated using C. antarctica B lipase as the protein scaffold. XRD analyses confirmed the formation of crystalline Pd(0) as the metal species in all cases. TEM analyses revealed spherical crystalline nanoparticles with average diameter size from 2 nm at 4 °C synthesis to 10 nm obtained at 50 °C synthesis. The thermal phenomenon was also critical in the final hybrid formation using more complex enzymes, where the relation of the protein structure and temperature and the influence of the latter has been demonstrated to be critical in the reducing efficiency of the enzyme in the final Pd nanoparticle formation, in the metal species, or even in the final size of the nanoparticles. Different Pd biohybrids were evaluated as catalysts in the C-H activation of protected l-tryptophan under mild conditions. Pd@CALB4 showed the best results, with >99% conversion for C-2 arylation in methanol at room temperature with a TOF value of 64 min−1, being 2 or 4 times higher than that of the other synthesized hybrids. This catalyst showed a very high stability and recyclability, maintaining >95% activity after three cycles of use. publishersversion published

Funding Information: The first author was partially funded by national funds through the Foundation for Science and Technology of Portugal, under the projects UIDB/00066/2020. The second author was supported by the Autonomous University of Mexico City (UACM) under the project Ccyt-2021-11. Publisher Copyright: © 2023 by the authors. Fractionalisation and solution of the Ambartsumian equation is considered. The general approach to fractional calculus suitable for applications in physics and engineering is described. It is shown that Liouville-type derivatives are the necessary ones, because they fully preserve backward compatibility with classical results. Such derivatives are used to define and solve the fractional Ambartsumian equation. First, a solution in terms of a slowly convergent fractional Taylor series is obtained. Then, a simple solution expressed in terms of an infinite linear combination of Mittag–Leffler functions is deduced. A fast algorithm, based on a bilinear transformation and using the fast Fourier transform, is described and demonstrated for its approximate numerical realisation. publishersversion published

Various social influences affect group decision-making processes. For instance, individuals may adapt their behavior to fit in with the group's majority opinion. Furthermore, ingroup favoritism may lead individuals to favor the ideas of ingroup members rather than the outgroup. So far, little is explored on how these phenomena of social conformity and ingroup favoritism manifest in group decision-making processes when a group has to decide in favor or against an item. We address such a scenario where the ‘flipping direction’ of conformity (in favor or against an item) matters. Specifically, we explore whether and how the ingroup favoritism manifests differently in terms of conformity behavior depending on the `flipping direction'. The results show that group inclusiveness does not play a role in the general tendency to conform. However, when it comes to a negative flipping direction, a higher feeling of group inclusiveness seems to play a role; yet, for individualist cultures only.

Efficient and reliable detection of damages is of critical importance in the aerospace industry. Within the context of non-destructive testing (NDT), advanced non-contact measurement techniques can be leveraged to obtain full-field responses of a target structure, facilitating the development of structural health monitoring (SHM) methodologies. The motivation of this chapter is to compare several types of measurement techniques, excitation signals, and post-processing methods applied to an Airbus airplane panel with sections of damaged and loose rivets. Its response is obtained using both digital image correlation (DIC) and laser Doppler vibrometry (LDV) techniques. The panel is excited with modal shakers or PZT patches using chirp or pseudorandom signals, for different frequency ranges. Full-field measurements were obtained, along with other more localized measurements on the area containing defects. On a later stage, methodologies inspired on nonlinear analysis and machine learning (ML) techniques were applied for damage detection. A description of the obtained results from these different experimental settings is shown in this chapter, together with a successful structural characterization and detection of damages on the airplane panel.

Funding Information: S.T., B.M., and J.C. contributed equally to this work. The authors are grateful for funding received from the Australian Research Council Centre of Excellence program (Project Number CE 140100012). J.C. acknowledges the European Research Council Starting Grant (ERC‐StG‐2019‐848325). S.N. and F.D. acknowledge the financial support of Australian Research Council through DP200102164. Publisher Copyright: © 2023 The Authors. Advanced Science published by Wiley-VCH GmbH. The deployment of structures that enable localized release of bioactive molecules can result in more efficacious treatment of disease and better integration of implantable bionic devices. The strategic design of a biopolymeric coating can be used to engineer the optimal release profile depending on the task at hand. As illustrative examples, here advances in delivery of drugs from bone, brain, ocular, and cardiovascular implants are reviewed. These areas are focused to highlight that both hard and soft tissue implants can benefit from controlled localized delivery. The composition of biopolymers used to achieve appropriate delivery to the selected tissue types, and their corresponding outcomes are brought to the fore. To conclude, key factors in designing drug-loaded biopolymeric coatings for biomedical implants are highlighted. publishersversion epub_ahead_of_print

It may be tempting for researchers to stick to incremental extensions of their current work to plan future research activities. Yet there is also merit in realizing the grand challenges in one's field. This paper presents an overview of the nine major research problems for the Business Process Management discipline. These challenges have been collected by an open call to the community, discussed and refined in a workshop setting, and described here in detail, including a motivation why these problems are worth investigating. This overview may serve the purpose of inspiring both novice and advanced scholars who are interested in the radical new ideas for the analysis, design, and management of work processes using information technology.

Climate response metrics are used to quantify the Earth's climate response to anthropogenic changes of atmospheric CO2. Equilibrium Climate Sensitivity (ECS) is one such metric that measures the equilibrium response to CO2 doubling. However, both in their estimation and their usage, such metrics make assumptions on the linearity of climate response, although it is known that, especially for larger forcing levels, response can be nonlinear. Such nonlinear responses may become visible immediately in response to a larger perturbation, or may only become apparent after a long transient. In this paper, we illustrate some potential problems and caveats when estimating ECS from transient simulations. We highlight ways that very slow timescales may lead to poor estimation of ECS even if there is seemingly good fit to linear response over moderate timescales. Moreover, such slow timescale might lead to late abrupt responses ("late tipping points") associated with a system's nonlinearities. We illustrate these ideas using simulations on a global energy balance model with dynamic albedo. We also discuss the implications for estimating ECS for global climate models, highlighting that it is likely to remain difficult to make definitive statements about the simulation times needed to reach an equilibrium., Comment: 27 pages, 14 figures

Research on open innovation (OI) has demonstrated the benefits of openness for firm innovation processes, but studies have mostly offered cross-sectional insights on incumbent firms. This study offers a more dynamic perspective on the relevance of OI for nascent ventures. Combining entrepreneurship and OI theories, we argue that it is key for resource-scarce nascent ventures to achieve critical venture-creation milestones. While OI can help these ventures to leverage salient external partnerships, we argue that it affects their speed of reaching these milestones. We test our hypotheses on a longitudinal sample focusing on external collaboration practices of nascent ventures in the renewable energy or information and communications technology industries. Our results show that, while engaging in R&D collaborations slows down nascent ventures’ product development and sustainable profit generation activities, joining industry associations does not have a slow-down effect. Our results complement the OI literature by warning about the downsides of openness for nascent ventures, particularly during the venture creation phase, where speed is a high priority.

This study presents the optimization and tuning of a simulation framework to improve its simulation accuracy while evaluating the energy utilization of electric buses under various mission scenarios. The simulation framework was developed using the low fidelity (Lo-Fi) model of the forward-facing electric bus (e-bus) powertrain to achieve the fast simulation speeds necessary for real-time fleet simulations. The measurement data required to verify the proper tuning of the simulation framework is provided by the bus original equipment manufacturers (OEMs) and taken from the various demonstrations of 12 m and 18 m buses in the cities of Barcelona, Gothenburg, and Osnabruck. We investigate the different methodologies applied for the tuning process, including empirical and optimization. In the empirical methodology, the standard driving cycles that have been used in previous studies to simulate various use case (UC) scenarios are replaced with actual driving cycles derived from measurement data from buses traversing their respective routes. The key outputs, including the energy requirements, total cost of ownership (TCO), and impact on the grid are statistically compared. In the optimization scenario, the assumptions for the various vehicle and mission parameters are tuned to increase the correlation between the simulation and measurement outputs (the battery SoC profile), for the given scenario input (the velocity profile). Improved simple optimization (iSOPT) was used to provide a superfast optimization process to tune the passenger load in the bus, cabin setpoint temperature, battery’s age as relative capacity degradation (RCD), SoC cutoff point between constant current (CC) and constant voltage charging (CV), charge decay factor used in CV charging, charging power, and cutoff in initial velocity during braking for which regenerative braking is activated.

Publisher Copyright: © 2013 IEEE. This research is supported by the Digital Manufacturing and Design Training Network (DiManD) project funded by the European Union through the Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2018) under grant agreement no. 814078 The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition. publishersversion published

Funding Information: Inês Ferreira acknowledges financial support from Fundação para a Ciência e Tecnologia for funding PhD Grant - REF: SFRH/BD/145448/2019. Helena Carvalho and Radu Godina acknowledge Fundação para a Ciência e a Tecnologia (FCT - MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI). Publisher Copyright: © 2022 The Authors The role of new technologies such as additive manufacturing and blockchain technology in designing and implementing circular economy ecosystems is not a trivial issue. This study aimed to understand if blockchain technology can be an enabler tool for developing additive symbiotic networks. A real case study was developed regarding a circular economy ecosystem in which a fused granular fabrication 3D printer is used to valorize polycarbonate waste. The industrial symbiosis network comprised four stakeholders: a manufacturing company that produces polycarbonate waste, a municipality service responsible for the city waste management, a start-up holding the 3D printer, and a non-profit store. It was identified a set of six requirements to adopt the blockchain technology in an additive symbiotic network, bearing in mind the need to have a database to keep track of the properties of the input material for the 3D printer during the exchanges, in addition to the inexistence of mechanisms of trust or cooperation between well-established industries and the additive manufacturing industry. The findings suggested a permissioned blockchain to support the implementation of the additive symbiotic network, namely, to enable the physical transactions (quantity and quality of waste material PC sheets) and monitoring and reporting (additive manufacturing technology knowledge and final product's quantity and price). Future research venues include developing blockchain-based systems that enhance the development of additive symbiotic networks. publishersversion published

Publisher Copyright: © 2013 IEEE. Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components -Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented. publishersversion published

Digital image correlation has recently seen a growing interest in both the research and industrial community thanks to the possibility to measure full-field information, with high confidence, and with a very limited instrumentation. Furthermore, advances in camera technology, particularly on resolution and data transfer rate, are now opening the door to new application, such as modal analysis and testing on rotating components. In this chapter, we give an overview of industrial applications of digital image correlation, ranging from the more classical characterization of material samples up to modal analysis and dynamic characterization of several components in stationary as well as operating conditions and show how the same instrumentation can be reused to cover multiple scenarios at the same time, without the need for changing instrumentation or data acquisition as is the case for other experimental techniques. We also show how DIC is used to characterize the static behavior of lattice structures, characterize both statically and dynamically mechanical components to validate numerical models, extract the modal behavior of rotating components, such as tires and fans, and finally understand the behavior of huge machines where the size and stiffness pose great limitations to the use of optical techniques.

Funding Information: This work is funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the projects UIDB/EEA/50008/2020 and 2022.03897.PTDC. Funding Information: We acknowledge the support of FCT/MCTES, as described above in funding. We also acknowledge the support of Autonoma TechLab for providing an interesting environment to carry out this research. Publisher Copyright: © 2022 by the authors. This is a tutorial on current techniques that use a huge number of antennas in intelligent reflecting surfaces (IRS), large intelligent surfaces (LIS), and radio stripes (RS), highlighting the similarities, differences, advantages, and drawbacks. A comparison between IRS, LIS, and RS is performed in terms of the implementation and capabilities, in the form of a tutorial. We begin by introducing the IRS, LIS, and RS as promising technologies for 6 G wireless technology. Then, we will look at how the three notions are applied in wireless networks. We discuss various performance indicators and methodologies for characterizing and improving the performance of IRS, LIS, and RS-assisted wireless networks. We cover rate maximization, power consumption reduction, and cost implementation concerns in order to take advantage of the performance increase. Furthermore, we extend the discussion to some cases of emerging use. In the description of the three concepts, IRS-assisted communication was introduced as a passive system, considering the capacity/data rate, with power optimization being an advantage, while channel estimation was a challenge. LIS is an active component that goes beyond massive MIMO; a recent study found that channel estimation issues in IRS had improved. In comparison to IRS, capacity enhancement is a highlight, and user interference showed a trend of decreasing. However, power consumption due to utilizing power amplifiers has restrictions. The third technique for increasing coverage is cell-free massive MIMO with RS, with easy deployment in communication network structures. It is demonstrated to have suitable energy efficiency and power consumption. Finally, for future work, we further propose expanding the conversation to include some cases of new uses, such as complexity reduction; design and simulation with LDPC code could be a solution to decreasing complexity. publishersversion published

Digitization is receiving a lot of interest in recent servitization research, but the use of platform-based Industry 4.0 technologies to boost product-service innovation (PSI) is less covered. This study aims to explore how companies successfully leverage platforms for servitization in an Industry 4.0 context. Building on theories of PSI, platform leverage and business model adaptation (BMA), we use longitudinal and interpretive research methods to conduct an exploratory study of the servitization pathways of four Chinese textile and apparel manufacturing companies. Results reveal companies’ roadmaps in undertaking digital and smart servitization strategies enabled by platform leveraging, and show the implementation approaches for related BMA. Further analysis identifies platform-based servitization destinations and pathway dynamics. This study constructs a theoretical basis and a typology for explaining platform-based servitization.

We demonstrated potential features of gold nanoparticle bipyramid (AuNB) for an electrochemical biosensor. The facile synthesis method and controllable shape and size of the AuNB are achieved through the optimization of cetyltrimethylammonium chloride (CTAC) surfactant over citric acid (CA) ratio determining the control of typically spherical Au seed size and its transition into a penta-twinned crystal structure. We observe that the optimized ratio of CTAC and CA facilitates flocculation control in which Au seeds with size as tiny as ∼14.8 nm could be attained and finally transformed into AuNB structures with an average length of ∼55 nm with high reproducibility. To improve the electrochemical sensing performance of a screen-printed carbon electrode, surface modification with AuNB via distinctive linking procedures effectively enhanced the electroactive surface area by 40%. Carried out for the detection of dopamine, a neurotransmitter frequently linked to the risk of Parkinson’s, Alzheimer’s, and Huntington’s diseases, the AuNB decorated-carbon electrode shows outstanding electrocatalytic activity that improves sensing performance, including high sensitivity, low detection limit, wide dynamic range, high selectivity against different analytes, such as ascorbic acid, uric acid and urea, and excellent reproducibility.

Detection of unwanted (`foreign') objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection. Deep learning has recently emerged as a powerful approach for recognizing patterns in radiographs (i.e., X-ray images), enabling automated X-ray based foreign object detection. However, these methods require a large number of training examples and manual annotation of these examples is a subjective and laborious task. In this work, we propose a Computed Tomography (CT) based method for producing training data for supervised learning of foreign object detection, with minimal labour requirements. In our approach, a few representative objects are CT scanned and reconstructed in 3D. The radiographs that have been acquired as part of the CT-scan data serve as input for the machine learning method. High-quality ground truth locations of the foreign objects are obtained through accurate 3D reconstructions and segmentations. Using these segmented volumes, corresponding 2D segmentations are obtained by creating virtual projections. We outline the benefits of objectively and reproducibly generating training data in this way compared to conventional radiograph annotation. In addition, we show how the accuracy depends on the number of objects used for the CT reconstructions. The results show that in this workflow generally only a relatively small number of representative objects (i.e., fewer than 10) are needed to achieve adequate detection performance in an industrial setting. Moreover, for real experimental data we show that the workflow leads to higher foreign object detection accuracies than with standard radiograph annotation., Comment: This paper is under consideration at Expert Systems with Applications. 22 pages, 15 figures

and also by the Polytechnic Institute of Lisbon through the Projects for Research, Development, Innovation and Artistic Creation (IDI&CA), within the framework of the project ReEdIA—Risk Assessment and Management in Open Innovation, IPL/2021/ReEdIA/ISEL. Publisher Copyright: © 2022 by the authors. This paper proposes a qualitative model to overcome the limitations of conventional failure mode and effects analysis (FMEA), which does not consider risk mitigation capabilities when prioritizing risks. Failure to consider these capabilities can lead to unrealistic risk estimates, especially when the level of uncertainty is high. In the proposed model, the original applicability of conventional FMEA was retained along with the three conventional risk variables, namely, severity, occurrence, and detectability. In addition, a fourth variable was added to account for risk mitigation capabilities. A case study in the project selection of aircraft repairs was used to demonstrate the implementation of the model and its applicability. The results show that the inclusion of mitigation options leads to more realistic risk scenarios, suggesting that the original FMEA approach may lead to non-conservative risk estimates. publishersversion published

Publisher Copyright: © 2013 IEEE. In this paper, we model the aggregate interference power in directional beamforming mobile networks. The work considers the random waypoint model to describe the mobility of the nodes and adopts directional beamforming for communication. The major contribution of this paper is the statistical characterization of the aggregate interference caused by directional beamforming transmissions of mobile interferers to a given node positioned at a reference point. The analysis assumes Rayleigh and Rician small-scale fading channels, a distance-based path-loss large-scale fading model, and a three gain levels sectored antenna model. The quality of the proposed approximations has been confirmed through various simulations for different mobility scenarios, channel conditions, and beamforming parameters, highlighting the effect of directional communications along with mobility on aggregate interference. To demonstrate the practical application of the work, we use two different estimators for the interference characterization. The results confirm the effectiveness of the estimators even when adopting a small set of samples. publishersversion published

This study aimed to develop and validate, based on the Evidence Centered Design approach, a generic tool to diagnose primary education pupils’ prior knowledge of technological systems in primary school classrooms. Two technological devices, namely the Buzz Wire device and the Stairs Marble Track, were selected to investigate whether theoretical underpinnings could be backed by empirical evidence. Study 1 indicated that the tool enabled pupils to demonstrate different aspects of their prior knowledge about a technological system by a wide variety of work products. Study 2 indicated that these work products could be reliably ranked from low to high functionality by technology education experts. Their rank order matched the Fischer-scale-based scoring rules, designed in cooperation with experts in skill development. The solution patterns fit the extended non-parametric Rasch model, confirming that the task can reveal differences in pupils’ prior knowledge on a one-dimensional scale. Test–retest reliability was satisfactory. Study 3 indicated that the diagnostic tool was able to capture the range of prior knowledge levels that could be expected of 10 to 12 years old pupils. It also indicated that pupils’ scores on standardised reading comprehension and mathematics test had a low predictive value for the outcomes of the diagnostic tool. Overall, the findings substantiate the claim that pupils’ prior knowledge of technological systems can be diagnosed properly with the developed tool, which may support teachers in decisions for their technology lessons about content, instruction and support.

Transdisciplinary (TD) working is claimed to be critical to meet future societal needs, with engineers being at the core to provide solutions to these challenges. However, there is little available that enables one to assess whether they or their team have the competencies required. Within this paper, we propose a self-assessment framework to ascertain whether design engineers have the competencies which enable TD working. We describe how the competencies were identified using a systematic literature review (SLR), we then describe how we utilized coded decision trees to classify which disciplinary level a particular competency can enable. In total, 76 competencies were classified; the results of the analysis show 20 of these displaying TD attributes as defined by Jantsch. The novelty of the approach is as follows: (1) In this paper, we propose a novel way to map the identified competencies against the levels of Jantsch’s hierarchical framework. (2) The proposed framework enables self-assessment of individual or team competencies to assess whether they have the competencies which enable TD working. (3) It enables a move towards incorporating TD practices in engineering projects.

Funding Information: This research was funded by Fundação para a Ciência e Tecnologia (FCT) with the grants SFRH/BD/36318/2007 and PTDC/DES/103178/2008 through CIPER (unit 369447). Publisher Copyright: © 2022 by the authors. Proton density (PD) and diffusion tensor imaging (DTI) are imaging techniques that enable the acquisition of data from living subjects that can be used in the fine-tuning of subject-specific models’ architectural parameters. The aim of this study was to determine the in vivo 3D architectural parameters (volume, pennation angle, fiber length and physiological cross-sectional area) of the gastrocnemius medialis, gastrocnemius lateralis, soleus and tibialis anterior muscles using proton density and diffusion tensor imaging data before and after an exhaustive one-legged jump exercise. These methods were used in the in vivo 3D data acquisition of six young and physically active female subjects’ lower legs, followed by a fiber-tracking algorithm and analysis tools. No significant differences were found in the muscles’ architecture after the exercise, with the following exceptions: the anatomical cross-section area of the gastrocnemius medialis increased (p-value 0.001, effect size 0.18) after exercise; the fiber lengths of the gastrocnemius medialis, lateralis and soleus muscles were higher after exercise (p-value 0.002, 0.001 and 0.001, respectively, and effect size 2.03, 1.29 and 0.85, respectively); and the soleus mean pennation angle decreased after exercise (p-value 0.0015, effect size 2.31). These changes (or lack thereof) could be attributed to the extended acquisition time of the MRI scans to minimize noise: by increasing the acquisition time, the effect of the exercise may have been partially lost due to muscle recovery. publishersversion published

Advanced science 9(27), 2201169 (2023). doi:10.1002/advs.202201169, Published by Wiley-VCH, Weinheim

Workshop aan een projectgroep bestaande uit zorgmanagers en zorgprofessionals die zich bezighoudt met het opstellen van een pakket van eisen voor de nieuwbouw van een woonzorgvoorziening voor kwetsbare ouderen die niet meer thuis kunnen wonen (De Hoven en Lentis)

The research work of this paper has been partially funded by the project VORTAL INTER DATA (n° 038361), co-financed by Vortal and COMPETE Program P2020. We would also like to thank UNIDEMI, DEMI, and LASI for providing us with the research infrastucture and resources to conduct this research. Publisher Copyright: © 2022 Elsevier Ltd Blockchain technologies have seen a steady growth in interest from industries as the technology is gaining maturity. It is offering a novel way to establish trust amongst multiple stakeholders without relying or trusting centralised authorities. While its use as a decentralised store of value has been validated through the emergence of cryptocurrencies, its use case in industrial applications with multiple stakeholder ecosystems such as industrial supply chain management, is still at an early stage of design and experimentation where private blockchains are used as opposed to public blockchains. Many enterprise blockchain projects failed to gain traction after initial launches, due to inefficient design, lack of incentives to all stakeholders or simply because the use of blockchain was not really necessary in the first place. There has been a need for a framework that allows blockchain designers and researchers to evaluate scenarios when a blockchain solution is useful and design the key configurations for an enterprise blockchain solution. Literature on blockchain architectures are sparse and only applicable to specific use cases or functionalities. This paper proposes a comprehensive Enterprise Blockchain Design Framework (EBDF), that not only identifies the relevant use cases when a blockchain must be utilised, but also details all the characteristics and configurations for designing an enterprise blockchain ecosystem, applicable to multiple industries. To validate the EBDF, we apply the same to the Vortal e-Procurement ecosystem allowing for multiple platforms to interoperate with greater transparency and accountability over the proposed blockchain framework. In this use case, many vendors bid for procurement procedures, often for publicly managed funds where it is extremely vital that full transparency and accountability is ensured in the entire process. Ensuring that certain digital certification functions, such as timestamps are independent from e-Procurement platform owners has been a challenge. Blockchain technology has emerged as a promising solution for not only ensuring transparency and immutability of records, but also providing for interoperability across different platforms by acting as a trusted third-party. The applied framework is used to design a Hyperledger based blockchain solution with some of the key architectural elements that could fulfil these needs while presenting the advantages of such a solution. publishersversion published

Funding Information: The authors acknowledge Fundação para a Ciência e a Tecnologia (FCT - MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI) and project KM3D (PTDC/EME-SIS/32232/2017). Publisher Copyright: © 2022 Elsevier Ltd Supply chains around the globe are susceptible to disturbances that negatively impact their performance. Generally, supply chain disturbances lead to failure modes that impact the ability of the supply chain to deliver the promised goods and services on time. Therefore, companies operating in different supply chains are willing to become resilient to disturbances and their ensuing failure modes to be able to deliver on time and remain competitive. In light of this willingness, this study aims to propose an index that enables companies to assess their resilience of on-time delivery to supply chain failure modes based on the resilience practices they deploy. To this end, drawing on the knowledge derived from case study data analysis and literature, eight propositions and an explanatory framework are put forward that theorize the identified relationships between supply chain disturbances, failure modes, resilience practices, and on-time delivery as the primary indicator for measuring supply chain performance. Next, considering the resilience practices companies tend to deploy, an index capable of assessing the companies’ resilience of on-time delivery to two prevalent supply chain failure modes, namely capacity shortage and material shortage is modelled and tested using a case study in an upstream automotive supply chain in Portugal. The results indicate high resilience levels of on-time delivery to the aforementioned failure modes, mainly due to the high cost of production halt in the automotive industry. Additionally, a set of supply chain capabilities and their related resilience practices and supply chain state variables are identified that can be deployed and controlled to improve supply chain resilience. publishersversion published

Colloidal heteronanocrystals allow for the synergistic combination of properties of different materials. For example, spatial separation of the photogenerated electron and hole can be achieved by coupling different semiconductors with suitable band offsets in one single nanocrystal, which is beneficial for improving the efficiency of photocatalysts and photovoltaic devices. From this perspective, axially segmented semiconductor heteronanorods with a type-II band alignment are particularly attractive since they ensure the accessibility of both photogenerated charge carriers. Here, a two-step synthesis route to Cu2-xS/CuInS2 Janus-type heteronanorods is presented. The heteronanorods are formed by injection of a solution of preformed Cu2-xS seed nanocrystals in 1-dodecanethiol into a solution of indium oleate in oleic acid at 240 °C. By varying the reaction time, Janus-type heteronanocrystals with different sizes, shapes, and compositions are obtained. A mechanism for the formation of the heteronanocrystals is proposed. The first step of this mechanism consists of a thiolate-mediated topotactic, partial Cu+ for In3+ cation exchange that converts one of the facets of the seed nanocrystals into CuInS2. This is followed by homoepitaxial anisotropic growth of wurtzite CuInS2. The Cu2-xS seed nanocrystals also act as sacrificial Cu+ sources, and therefore, single composition CuInS2 nanorods are eventually obtained if the reaction is allowed to proceed to completion. The two-stage seeded growth method developed in this work contributes to the rational synthesis of Cu2-xS/CuInS2 heteronanocrystals with targeted architectures by allowing one to exploit the size and faceting of premade Cu2-xS seed nanocrystals to direct the growth of the CuInS2 segment.

Copper-doped II-VI and copper-based I-III-VI2colloidal quantum dots (CQDs) have been at the forefront of interest in nanocrystals over the past decade, attributable to their optically activated copper states. However, the related recombination mechanisms are still unclear. The current work elaborates on recombination processes in such materials by following the spin properties of copper-doped CdSe/CdS (Cu@CdSe/CdS) and of CuInS2and CuInS2/(CdS, ZnS) core/shell CQDs using continuous-wave and time-resolved optically detected magnetic resonance (ODMR) spectroscopy. The Cu@CdSe/CdS ODMR showed two distinct resonances with different g factors and spin relaxation times. The best fit by a spin Hamiltonian simulation suggests that emission comes from recombination of a delocalized electron at the conduction band edge with a hole trapped in a Cu2+site with a weak exchange coupling between the two spins. The ODMR spectra of CuInS2CQDs (with and without shells) differ significantly from those of the copper-doped II-VI CQDs. They are comprised of a primary resonance accompanied by another resonance at half-field, with a strong correlation between the two, indicating the involvement of a triplet exciton and hence stronger electron-hole exchange coupling than in the doped core/shell CQDs. The spin Hamiltonian simulation shows that the hole is again associated with a photogenerated Cu2+site. The electron resides near this Cu2+site, and its ODMR spectrum shows contributions from superhyperfine coupling to neighboring indium atoms. These observations are consistent with the occurrence of a self-trapped exciton associated with the copper site. The results presented here support models under debate for over a decade and help define the magneto-optical properties of these important materials.