Search

Battery energy storage systems (BESS) have been extensively investigated to improve the efficiency, economy, and stability of modern power systems and electric vehicles (EVs). However, it is still challenging to widely deploy BESS in commercial and industrial applications due to the concerns of battery aging. This paper proposes an integrated battery life loss modeling and anti-aging energy management (IBLEM) method for improving the total economy of BESS in EVs. The quantification of BESS aging cost is realized by a multifactorial battery life loss quantification model established by capturing aging characteristics from cell acceleration aging tests.Meanwhile, a charging event analysis method is proposed to deploy the built life loss model in vehicle BESS management. Two BESS active anti-aging vehicle energy management models: vehicle to grid (V2G) scheduling and plug-in hybrid electric vehicle (PHEV) power distribution, are further designed, where the battery life loss quantification model is used to generate the aging cost feedback signals. The performance of the developed method is validated on a V2G peak-shaving simulation system and a hybrid electric vehicle. The work in this paper presents a practical solution to quantify and mitigate battery aging costs by optimizing energy management strategies and thus can further promote transportation electrification.

Data plane switches in software-defined networks are increasingly recognised as potential targets for attack, with recent exploits showing their vulnerability to full compromise. The serious consequences of such a breach have prompted the design of compromise detection mechanisms, which monitor switch forwarding behaviour at runtime to ensure that it has not been altered by an attack. However, such defences cannot achieve full coverage in stateful, programmable data planes, creating an opportunity for an attacker to evade detection by carefully editing a switch's forwarding program to mishandle a small subset of packets. To exploit this opportunity and avoid detection, an attacker must analyse and edit the program's behaviour within a narrow time window, which is possible when the data plane is defined by a uBPF program compiled from P4, due to the predictable compilation process. In this work, we aim to invalidate this analysis-guided attack technique with targeted obfuscation of P4-uBPF programs that increases the analysis complexity. We find that, by inserting additional program paths and syntactic dependencies between variables, we can force an attacker to analyse a higher proportion of program instructions and carry out time-consuming SMT solving to find valid program paths, rendering the previous attack technique infeasible. Furthermore, by applying our identified program optimisations, program performance can often be maintained after obfuscation. In evaluating our work, we identify the potential to improve our solution by tailoring obfuscations to individual program paths.

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

During the last years, the “Great Online Transition” has brought to light large variation in teachers' readiness for online teaching and learning (OTL). Drawing from an international sample of 731 higher-education teachers, we examined gender differences in OTL readiness as a source of this variation. Currently, in the field of OTL, better evidence is needed to understand the associated dimensions and effects of gender on teachers’ experiences and perceptions of readiness, to provide better support and professional learning opportunities in transitioning to an online and blended practice. To provide such evidence, we first evaluated the measurement bias in the readiness measures and found support for strong gender invariance. Second, we quantified the gender differences in readiness levels: Women reported higher readiness for cognitive activation practices (d = +0.15); men reported higher self-efficacy in technological content knowledge (d = −0.20). These gender differences were small, varied across readiness constructs, and were due to a gender gap in OTL experience. Third, construct associations involving perceived institutional support were weaker for women. To improve the quality, robustness, and validity of the respective evidence, we argue that studying gender divides in OTL readiness needs to consider measurement bias, OTL experience, and construct associations.

The long-term uncertainty of multi-energy demand poses significant challenges to the coordinated pricing of multiple energy systems (MES). This paper proposes an integrated network pricing methodology for MES based on the long-run-incremental cost (LRIC) to recover network investment costs, affecting the siting and sizing of future distributed energy resources (DERs) and incentivizing the efficient utilization of MES. The stochasticity of multi-energy demand growth is captured by the Geometric Brownian Motion (GBM)-based model. Then, it is integrated with a system operation model to minimize operation costs, considering low-carbon targets and flexible demand. Thereafter, the kernel density estimation (KDE) method is used to perform the probabilistic optimal energy flow (POEF) to obtain energy flows under uncertain load conditions. Based on the probability density functions (PDFs) of energy flows, an LRIC-based network pricing model is designed, where Tail Value at Risk (TVaR) is used to model the risks of loading levels of branches and pipelines. The performance of the proposed methodology is validated on a typical MES. The proposed pricing method can stimulate cost-effective planning and utilization of MES infrastructures under long-term uncertainty, thus helping reduce low-carbon transition costs.

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

Funding Information: The first and third authors were financially supported by the Fundação para a Ciência e a Tecnologia, Portugal (Portuguese Foundation for Science and Technology) through the projects UIDB/MAT/00297/2020 , UIDP/MAT/00297/2020 (Centro de Matemática e Aplicações), and PTDC/CCI-BIO/4180/2020 . The second author was financially supported by the Forest Research Center, a research unit funded by Fundação para a Ciência e a Tecnologia (FCT), Portugal , through the project ( UIDB/00239/2020 ). Publisher Copyright: © 2023 The Author(s) Consider a graph with vertex set V and non-negative weights on the edges. For every subset of vertices S, define ϕ(S) to be the sum of the weights of edges with one vertex in S and the other in V∖S, minus the sum of the weights of the edges with both vertices in S. We consider the problem of finding S⊆V for which ϕ(S) is maximized. We call this combinatorial optimization problem the max-out min-in problem (MOMIP). In this paper we (i) present a linear 0/1 formulation and a quadratic unconstrained binary optimization formulation for MOMIP; (ii) prove that the problem is NP-hard; (iii) report results of computational experiments on simulated data to compare the performances of the two models; (iv) illustrate the applicability of MOMIP for two different topics in the context of data analysis, namely in the selection of variables in exploratory data analysis and in the identification of clusters in the context of cluster analysis; and (v) introduce a generalization of MOMIP that includes, as particular cases, the well-known weighted maximum cut problem and a novel problem related to independent dominant sets in graphs. publishersversion published

Funding Information: This work was done under the project “CardioFollow.AI: An intelligent system to improve patients’ safety and remote surveillance in follow-up for cardiothoracic surgery”. Publisher Copyright: © 2023 The Author(s) Clinical data are essential in the medical domain. However, their heterogeneous nature leads to many data quality problems, notably missing values, which undermine the performance of Machine Learning-based clinical systems. Hence, there has been a growing interest in strategies that address this challenge in order to build trustworthy systems to improve the quality of care and benefit clinical decision-making. In particular, missing value imputation is a common approach. This paper proposes three novel imputation techniques that leverage correlation in an innovative manner by exploring the relationship between values and missingness patterns. Experiments were carried out on three publicly available datasets, under three missingness mechanisms with different missing rates, and on two real-world medical datasets. The imputation precision and the classification performance of the proposed techniques were evaluated in a comprehensive comparative study, which included diverse existing methods. The developed techniques outperformed state-of-the-art methods on several assessments while overcoming current flaws shared by correlation-based imputation strategies in real-world medical problems. publishersversion published

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.

We give a new proof of a theorem of Mints that the positive fragment of minimal predicate logic is decidable. The idea of the proof is to replace the eigenvariable condition of sequent calculus by an appropriate scoping mechanism. The algorithm given by this proof seems to be more practical than that given by the original proof. A naive implementation is given at the end of the paper. Another contribution is to show that this result extends to a large class of theories, including simple type theory (higher-order logic) and second-order propositional logic. We obtain this way a new proof of the decidability of the inhabitation problem for positive types in system F.

Digitale transformatie is een maatschappelijke opdracht voor de Hanzehogeschool Groningen. Niet alleen in het kader van de bedrijfsvoering maar ook binnen het onderwijs en onderzoek. Doordat digitale transformatie impact heeft op de beroepen waarvoor de Hanze opleidt en daarmee op de vaardigheden die van afgestudeerden gevraagd worden, heeft het ook invloed op de vaardigheden die docenten onderwijzen en de onderliggende systemen en processen van de organisatie. Annalies Kootstra, programmamanager digitale transformatie, vertelt aan de hand van concrete voorbeelden over de veelzijdigheid van de digitale transformatieopdracht bij de Hanze hogeschool.

Our study focuses on myths about older people’s smartphone use. Self-reported data, from Eurostat for example, report access rather than actual usage and are of limited use. What respondents report does not necessarily correspond with their actual smartphone usage behaviour in everyday life. We therefore conducted a tracking study to gain insight into smartphone usage among older adults. Smartphone activity logs were collected from individuals aged 60–79 (N = 303) throughout a period of 28 days between February and May 2019 in Canada, the Netherlands, Spain and Sweden. The data thus obtained on actual smartphone use were critically examined in the light of seven myths related to the smart phone usage of older people in everyday life. We also analysed the data in the context of empirical studies in the field of older people’s digital behaviour. Finally, after drawing our conclusions, we present limitations and sketch implications for future research.

In fault tolerance for parallel and distributed systems, message logging protocols have played a prominent role in the lastthree decades. Such protocols enable local rollback to provide recovery from fail-stop errors. Global rollback techniques can bestraightforward to implement but at times lead to slower recovery than local rollback. Local rollback is more complicated but can offerfaster recovery times. In this work, we study the power and energy efficiency implications of global and local rollback. We propose apower-efficient version of local rollback to reduce power consumption for non-critical, blocked processes, using Dynamic Voltage andFrequency Scaling (DVFS) and clock modulation (CM). Our results for 3 different MPI codes on 2 parallel systems show thatpower-efficient local rollback reduces CPU energy waste up to 50% during the recovery phase, compared to existing global and localrollback techniques, without introducing significant overheads. Furthermore, we show that savings manifest for all blocked processes,which grow linearly with the process count. We estimate that for settings with high recovery overheads the total energy waste ofparallel codes is reduced with the proposed local rollback.

The way the human body is depicted in classical and modern paintings is relevant for art historical analyses. Each artist has certain themes and concerns, resulting in different poses being used more heavily than others. In this paper, we propose a computer vision pipeline to analyse human pose and representations in paintings, which can be used for specific artists or periods. Specifically, we combine two pose estimation approaches (OpenPose and DensePose, respectively) and introduce methods to deal with occlusion and perspective issues. For normalisation, we map the detected poses and contours to Leonardo da Vinci's Vitruvian Man, the classical depiction of body proportions. We propose a visualisation approach for illustrating the articulation of joints in a set of paintings. Combined with a hierarchical clustering of poses, our approach reveals common and uncommon poses used by artists. Our approach improves over purely skeleton based analyses of human body in paintings.

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

Data gathered from 357 undergraduate and graduate teacher education students were analyzed to assess the ability of the Synthesis of Qualitative Data (SQD) instrument to measure six teaching strategies (role models, reflection, collaboration, feedback, instructional design and authentic experiences) important for preparing future teachers to use technology in their future teaching. Factor analysis, multidimensional scaling, and analysis of variance techniques were applied to the 24 SQD items developed through qualitative assessment of effective strategies needed to prepare teachers, in order to assess reliability and validity for six unique four-item scales. Findings were that all scales exhibited acceptable performance characteristics as independent measures, implying that future studies could use subsets of the items rather than the full 24-item instrument. Recommended areas of future research and practical implications for teacher preparation programs are discussed.

GPS technology and tracking study designs have gained popularity as a tool to go beyond the limitations of static exposure assessments based on the subject's residence. These dynamic exposure assessment methods offer high potential upside in terms of accuracy but also disadvantages in terms of cost, sample sizes, and types of data generated. Because of that, with our study we aim to understand in which cases researchers need to use GPS-based methods to guarantee the necessary accuracy in exposure assessment. With a sample of 113 seniors living in Barcelona (Spain) we compare their estimated daily exposures to air pollution (PM2.5, PM10, NO2), noise (dB), and greenness (NDVI) using static and dynamic exposure assessment techniques. Results indicate that significant differences between static and dynamic exposure assessments are only present in selected exposures, and would thus suggest that static assessments using the place of residence would provide accurate-enough values across a number of exposures in the case of seniors. Our models for Barcelona’s seniors suggest that dynamic exposure would only be required in the case of exposure to smaller particulate matter (PM2.5) and exposure to noise levels. The study signals to the need to consider both the mobility patterns and the built environment context when deciding between static or dynamic measures of exposure assessment.

Paths $P^1,\ldots,P^k$ in a graph $G=(V,E)$ are mutually induced if any two distinct $P^i$ and $P^j$ have neither common vertices nor adjacent vertices. For a fixed integer $k$, the $k$-Induced Disjoint Paths problem is to decide if a graph $G$ with $k$ pairs of specified vertices $(s_i,t_i)$ contains $k$ mutually induced paths $P^i$ such that each $P^i$ starts from $s_i$ and ends at $t_i$. Whereas the non-induced version is well-known to be polynomial-time solvable for every fixed integer $k$, a classical result from the literature states that even $2$-Induced Disjoint Paths is NP-complete. We prove new complexity results for $k$-Induced Disjoint Paths if the input is restricted to $H$-free graphs, that is, graphs without a fixed graph $H$ as an induced subgraph. We compare our results with a complexity dichotomy for Induced Disjoint Paths, the variant where $k$ is part of the input.

Funding Information: We acknowledge the support from the European Commission through the ETHNA System project (Grant Nº 872360). Partial financial support was also provided by Portuguese FCT program UIDB/00066/2020 (CTS – Center of Technology and Systems). Publisher Copyright: © 2023, The Author(s). New organizational forms are emerging today at all levels of society, and more and more research is conducted in dynamic collaborative networks or ecosystems. Unlike traditional research centers, these new types of organization are very dynamic, with fluid boundaries, and volatile in terms of membership. This characteristic requires that more attention be paid to research ethics and RRI. This work reports on an implementation process carried out in a research and innovation ecosystem according to the principles and guidelines proposed by the ETHNA project. The process, its barriers and drivers are described, and finally, learned lessons and recommendations are presented. publishersversion published

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.

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

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.

A series of recent papers introduce the concept of Forecast Reconciliation, a process by which independently generated forecasts of a collection of linearly related time series are reconciled via the introduction of accounting aggregations that naturally apply to the data. Aside from its clear presentational and operational virtues, the reconciliation approach generally improves the accuracy of the combined forecasts. In this paper, we examine the mechanisms by which this improvement is generated by re-formulating the reconciliation problem as a combination of direct forecasts of each time series with additional indirect forecasts derived from the linear constraints. Our work establishes a direct link between the nascent Forecast Reconciliation literature and the extensive work on Forecast Combination. In the original hierarchical setting, our approach clarifies for the first time how unbiased forecasts for the entire collection can be generated from base forecasts made at any level of the hierarchy, and we illustrate more generally how simple robust combined forecasts can be generated in any multivariate setting subject to linear constraints. In an empirical example, we show that simple combinations of such forecasts generate significant improvements in forecast accuracy where it matters most: where noise levels are highest and the forecasting task is at its most challenging.

A major challenge in AI is designing autonomous systems that capture the values of stakeholders, and do so in such away that one can assess the extent to which that system’s behaviour is aligned to those values. In this paper we discuss our response to this challenge that is both practical and built on clear principles. Specifically, we propose eleven heuristics to organise the process of making values operational in the design of particular class of AI systems called online institutions. These are governed systems of interacting communities of human and autonomous artificial agents.

Native Language Identification is a prominent paralinguistic study with applications ranging from biometric analysis to speaker adaptation. Former studies on this task have benefited from alternative acoustic feature representations and pre-trained neural networks. In this work, we explore the Native Language Identification performance of contextual acoustic (wav2vec 2.0) and linguistic (BERT) embeddings as state-of-the-art feature representations and combine them with acoustic features at different levels. We encode acoustic and linguistic features using Fisher Vectors, applying Fisher Vector encoding on BERT word embeddings and wav2vec 2.0 for the first time for a paralinguistic task. We compare this approach with conventional functional summarization. In line with our former study using only acoustic modality, the results indicate the superiority of Fisher Vectors encoding over the traditional techniques. Moreover, we show the efficacy of combining alternative representations now in both acoustic and linguistic modalities. Results indicate a notable contribution of the transformer-based contextual auditory and linguistic feature representations to bimodal Native Language Identification systems.

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 paper introduces a multi-period, two-dimensional vehicle loading and dispatching problem, called Two-Dimensional Vehicle Loading and Dispatching Problem with Incompatibility Constraints (VLDP). The problem concerns preparing a single-origin single-destination transportation plan of loading required orders to vehicles at the origin and dispatching the vehicles to deliver the orders to the destination within their due dates. The decision maker uses their own fleet of vehicles, with each vehicle having a fixed transportation cost per trip, and may outsource additional vehicles at a higher cost. VLDP involves constraints regarding the due dates of the orders, pairwise incompatibility of orders packed in the same vehicle, incompatibility of orders and vehicles, as well as area and weight capacity of the vehicles. An order can be delivered earlier than its due date, incurring an earliness penalty due to storage requirements at the destination. The objective is to minimize the total vehicle usage and earliness penalty costs. A Mixed-Integer Linear Programming model (MILP) is provided, as well as an Adaptive Large Neighbourhood Search (ALNS) algorithm. Results of computational experiments on instances derived from real-world data show the effectiveness of the ALNS algorithm.

Funding: This work is partly funded by NSF award OAC-1839900. This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357. libEnsemble was developed as part of the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. This research used resources of the OLCF at ORNL, which is supported by the Office of Science of the U.S. DOE under Contract No. DE-AC05-00OR22725. Lightning talks of the Workflows in Support of Large-Scale Science (WORKS) workshop are a venue where the workflow community (researchers, developers, and users) can discuss work in progress, emerging technologies and frameworks, and training and education materials. This paper summarizes the WORKS 2022 lightning talks, which cover five broad topics: data integrity of scientific workflows; a machine learning-based recommendation system; a Python toolkit for running dynamic ensembles of simulations; a cross-platform, high-performance computing utility for processing shell commands; and a meta(data) framework for reproducing hybrid workflows. Postprint

We show that some natural problems that are XNLP-hard (hence $$\textrm{W}[t]$$ -hard for all t) when parameterized by pathwidth or treewidth, become FPT when parameterized by stable gonality, a novel graph parameter based on optimal maps from graphs to trees. The problems we consider are classical flow and orientation problems, such as Undirected Flow with Lower Bounds, Minimum Maximum Outdegree, and capacitated optimization problems such as Capacitated (Red-Blue) Dominating Set. Our hardness claims beat existing results. The FPT algorithms use a new parameter “treebreadth”, associated to a weighted tree partition, as well as DP and ILP.

In intelligent video surveillance under complex scenes, it is vital to identify the current actions of multi-target human bodies accurately and in real time. In this paper, a real-time multi-person action recognition method with monocular vision is proposed based on sequence models. Firstly, the key points of multi-target human body skeleton in the video are extracted by using the OpenPose algorithm. Then, the human action features are constructed, including limb direction vector and the skeleton height-width ratio. The multi-target human bodies tracking is then achieved by using the tracking algorithm. Next, the tracking results are matched with the action features, and the action recognition model is constructed, which includes the spatial branch based on Deep neural networks and the temporal branch based on Bi-directional RNN and Bi-directional long short-term memory networks. After pre-training, the model can be used to recognize the human body action from action features, and a recognition stabilizer is designed to minimize false alarms. Finally, extensive evaluations on the JHMDB dataset validate the effectiveness and the superiority of the proposed approach.

In line with the growing popularity of process mining, several methodologies have been proposed to guide the conduct of process mining projects. Such methodologies reason that process mining projects start with a concrete question. However, in practice we observe projects with a different starting point, often aimed at exploring the data. Existing methodologies provide limited aid in such situations, and as a result, we wonder: how are questions developed within process mining projects? In this paper, we present the results of an interview study that sheds light on question development in process mining. We provide insights from expert interviewees, resulting in six recommendations that enhance existing methodologies. In doing so, we present concrete examples of how process mining analyses can support question formulation and refinement.

Silva, D., & Bacao, F. (2022). MapIntel: Enhancing Competitive Intelligence Acquisition Through Embeddings and Visual Analytics. In G. Marreiros, B. Martins, A. Paiva, A. Sardinha, & B. Ribeiro (Eds.), Progress in Artificial Intelligence: 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, August 31–September 2, 2022, Proceedings (pp. 599-610). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13566 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16474-3_49 -- Funding Information: This work was supported by the Fundação para a Ciência e Tecnologia of Ministério da Ciência e Tecnologia e Ensino Superior (research grant under the DSAIPA/DS/0116/2019 project). Competitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision-making. The system searching module uses a retriever and re-ranker engine that first finds the closest neighbors to the query embedding and then sifts the results through a cross-encoder model that identifies the most relevant documents. The browsing module also leverages the embeddings by projecting them onto two dimensions while preserving the original landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modeling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. In this work, we evaluate the system and its components on the 20 newsgroups dataset and demonstrate the superiority of Transformer-based components. authorsversion published

Silva, H., António, N., & Bacao, F. (2022). A Rapid Semi-automated Literature Review on Legal Precedents Retrieval. In G. Marreiros, B. Martins, A. Paiva, B. Ribeiro, & A. Sardinha (Eds.), Progress in Artificial Intelligence: 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, August 31–September 2, 2022, Proceedings (pp. 53-65). (Lecture Notes in Artificial Intelligence; Vol. 13566). Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_5. Funding Information: This research was supported by a grant from the Portuguese Foundation for Science and Technology (“Fundação para a Ciência e a Tecnologia”) [grant number DSAIPA/DS/0116/2019]. Precedents constitute the starting point of judges’ reasoning in national legal systems. Precedents are also an essential input for case-based reasoning (CBR) methodologies. Although considerable research has been done on CBR applied to legal practice, the precedent retrieval techniques are a relatively new and unexplored field of AI & Law. Only a few works have tested or developed methods for identifying such previous similar cases. This work uses text mining (TM), natural language processing (NLP), and data visualization methods to provide a semi-automated rapid literature review and identify how justice courts and legal practitioners may use AI to retrieve similar cases. Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), automation techniques were used to expedite the literature review. In this study, we confirmed the feasibility of automation tools for expediting literature reviews and provided an overview of the current research state on legal precedents retrieval. authorsversion published

Workarounds can give valuable insights into the work processes that are carried out within organizations. To date, workarounds are usually identified using qualitative methods, such as interviews. We propose the semi-automated WORkaround Detection (SWORD) framework, which takes event logs as input. This extensible framework uses twenty-two patterns to semi-automatically detect workarounds. The value of the SWORD framework is that it can help to identify workarounds more efficiently and more thoroughly than is possible by the use of a more traditional, qualitative approach. Through the use of real hospital data, we demonstrate the applicability and effectiveness of the SWORD framework in practice. We focused on the use of three patterns, which all turned out to be applicable to the characteristics of the data set. The use of two of these patterns also led to the identification of actual workarounds. Future work is geared to the extension of the patterns within the framework and the enhancement of techniques that can help to identify these in real-world data.

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

Funding Information: This work was partially supported by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) through Institute for Systems and Robotics (ISR), under Laboratory for Robotics and Engineering Systems (LARSyS) project UIDB/50009/2020, through project PCIF/MPG/0156/2019 FirePuma and through COPELABS, University Lusófona project UIDB/04111/2020. Publisher Copyright: © 2022 Elsevier B.V. In this paper, we tackle the problem of state estimation for uncertain linear systems when bounds are known for the disturbances, noise and initial state. Practical systems often have parameters that cannot be measured precisely at every iteration. The framework of Uncertain Linear Parameter-Varying systems (Uncertain LPVs) have attracted attention from the community and have seen applications from the aerospace industry to mechatronic systems, among many other examples. By formulating the problem as the solution of a feasibility program, we show that the optimal convex solution can be computed through an enumeration of the vertices of the estimates. Resorting to this result, three algorithms are proposed: an approximation algorithm using only set operations; an exact convex hull method returning the optimal convex set suitable for cases where estimates do not have a large number of vertices; and an event-triggering algorithm suitable for fault/attack detection that combines both the convex and nonconvex methods. Simulations are conducted using a motor speed model where some of the parameters cannot be measured exactly pointing out that the uncertainty matrices are responsible for the accuracy of the approximation algorithm, and also that the point-based method is suitable for online estimation. publishersversion published

Glioblastoma is profoundly heterogeneous in regional microstructure and vasculature. Characterizing the spatial heterogeneity of glioblastoma could lead to more precise treatment. With unsupervised learning techniques, glioblastoma MRI-derived radiomic features have been widely utilized for tumor sub-region segmentation and survival prediction. However, the reliability of algorithm outcomes is often challenged by both ambiguous intermediate process and instability introduced by the randomness of clustering algorithms, especially for data from heterogeneous patients. In this paper, we propose an adaptive unsupervised learning approach for efficient MRI intra-tumor partitioning and glioblastoma survival prediction. A novel and problem-specific Feature-enhanced Auto-Encoder (FAE) is developed to enhance the representation of pairwise clinical modalities and therefore improve clustering stability of unsupervised learning algorithms such as K-means. Moreover, the entire process is modelled by the Bayesian optimization (BO) technique with a custom loss function that the hyper-parameters can be adaptively optimized in a reasonably few steps. The results demonstrate that the proposed approach can produce robust and clinically relevant MRI sub-regions and statistically significant survival predictions.

In the order fulfilment and retail industry, the provision of fast and low cost delivery services has received significant focus over the last decade. What is often neglected is other factors that contribute to a customer’s overall experience like customisation and flexibility of deliveries, especially in cases when a customer’s needs and plans change between order placement and order delivery. This paper explores the value of a flexible type of logistics offering in which the customer is able to change the order requirements (such as delivery date) after the order has been placed. In particular, we focus on the interactions between a retailer and a customer in agreeing and executing such logistics orders. We examine the benefits of this type of flexible logistics offerings because flexibility and customisation are critical factors for the success of omni-channel commerce. Such offerings are clearly connected with recent developments in digital technologies as these technologies are essential for the implementation. Our results indicate that flexible logistics offerings can be beneficial both for retailers and for customers. They also highlight the importance of receiving orders that do not fully meet the requirements of a customer.

Inverter-based resources (IBRs) possess dynamics that are significantly different from those of synchronous-generator-based sources and as IBR penetrations grow the dynamics of power systems are changing. This article discusses the characteristics of the new dynamics and examines how they can be accommodated into the long-standing categorizations of power system stability in terms of angle, frequency, and voltage stability. It is argued that inverters are causing the frequency range over which angle, frequency, and voltage dynamics act to extend such that the previously partitioned categories are now coupled and further coupled to new electromagnetic modes. While grid-forming (GFM) inverters share many characteristics with generators, grid-following (GFL) inverters are different. This is explored in terms of similarities and differences in synchronization, inertia, and voltage control. The concept of duality is used to unify the synchronization principles of GFM and GFL inverters and, thus, established the generalized angle dynamics. This enables the analytical study of GFM-GFL interaction, which is particularly important to guide the placement of GFM apparatuses and is even more important if GFM inverters are allowed to fall back to the GFL mode during faults to avoid oversizing to support short-term overload. Both GFL and GFM inverters contribute to voltage strength but with marked differences, which implies new features of voltage stability. Several directions for further research are identified, including: 1) extensions of nonlinear stability analysis to accommodate new inverter behaviors with cross-coupled time frames; 2) establishment of spatial–temporal indices of system strength and stability margin to guide the provision of new stability services; and 3) data-driven approaches to combat increased system complexity and confidentiality of inverter models.

Castelo-Branco, I., Oliveira, T., Coelho, P. S., Portugal, J., & Filipe, I. (2022). Measuring the fourth industrial revolution through the Industry 4.0 lens: The relevance of resources, capabilities and the value chain. Computers in Industry, 138(June), [103639]. https://doi.org/10.1016/j.compind.2022.103639 --------- Funding Information: The quantitative part of this work was funded through grant AAC 3/SIAC/2017-035947 from Portugal COMPETE program. The present study presents an Industry 4.0 measurement model that is applied to a sample of Portuguese companies from several economic sectors beyond manufacturing. An Industry 4.0 measurement scale is developed through a three-step methodology that relies on a mixed-methods approach. Five constructs are identified from literature, together with respective measurement indicators: IT strategy and cybersecurity, enablers, smart factory, value proposition and customer experience. These constructs are subsequently deepened in a qualitative study where second order dimensions are identified and the measurement scale is extended and validated. The model is applied in a quantitative study, the conclusions of which suggest some realignment in the initial constructs. Finally, an Industry 4.0 maturity index is derived. Industry 4.0 maturity is measured from the firms' ability to deploy capabilities and resources to impact on the value chain. publishersversion published

Nunes, C., Oliveira, T., Santini, F. D. O., Castelli, M., & Cruz-jesus, F. (2022). A Weight and Meta-Analysis on the Academic Achievement of High School Students. Education Sciences, 12(5), 1-17. [287]. https://doi.org/10.3390/educsci12050287 Understanding the determinants of academic achievement (AA) is crucial for virtually every stakeholder interested in personal development and individual and societal wellbeing. Extensive research in several areas, such as education, economics, or psychology, has addressed this topic, identifying a vast number of determinants that impact high school students’ AA. In this work, we perform a meta-analysis, including a weight analysis of 49 quantitative studies that investigate this topic, exploring the best predictors of high school students’ academic success. We also explore moderation effects. Our results show that academic self-efficacy and socioeconomic status are the best predictors of AA, and they are statistically significant. Other statistically significant predictors, albeit less common in the analyses, are mastery avoidance, motivation, sleep habits, and work avoidance. Implications for theory and practice and directions for future research are discussed publishersversion published