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Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major component of plant phenotyping, which can be used to measure the growth of the plant. Therefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net. The original plant image and segmented leaf parts are fed as input because the segmented leaf part provides additional information to the proposed LC-Net. The well-known SegNet model has been utilised to obtain segmented leaf parts because it outperforms four other popular Convolutional Neural Network (CNN) models, namely DeepLab V3+, Fast FCN with Pyramid Scene Parsing (PSP), U-Net, and Refine Net. The proposed LC-Net is compared to the other recent CNN-based leaf counting models over the combined Computer Vision Problems in Plant Phenotyping (CVPPP) and KOMATSUNA datasets. The subjective and numerical evaluations of the experimental results demonstrate the superiority of the LC-Net to other tested models., Funding Agencies|Linkping University

Eye tracking is a common tool to assess drivers' attentional state, either in real-time with the goal to prevent incidents, or offline, to understand underlying processes. While seemingly objective, eye tracking data can be coded and interpreted in different ways, which can have substantial effects on the results. The objective of this paper is to highlight and discuss the possibilities and limitations of three different approaches to code glance data: the direction-based encoding, the target-based approach, and the purpose-based approach. The direction-based coding scheme de-scribes glances relative to the direction of travel. The target-based approach classifies the glance targets into different categories. The purpose-based approach needs additional layers of information to deduce the reason for the glance. This information encompasses road layout, traffic rules, and the presence and relevance of other traffic. Data from a field study with 23 participants driving an instrumented vehicle on an urban route was used to illustrate differences between the three methods. The results showed that the coding approach clearly affected the interpretation of the measured glance data. A purely target-based approach is limited by its inability to account for spare visual capacity and that also the absence of a target constitutes valuable information, and a purely direction-based approach does not account for the need to scan areas located off forward. The purpose-based approach requires contextual information that can be cumbersome to integrate. Regardless of the approach used, additional layers of contextual information increase understanding and interpretability, potentially at the cost of increased complexity. The three approaches are suitable for different contexts and their feasibility also depends on the availability of additional data. A key message is that context awareness improves the accuracy of driver attention monitoring and inattention identification., Funding Agencies|Swedish Strategic Vehicle Research and Innovation Programme (FFI) [2017-05526]

In this paper, the Hierarchical Multi Switch Multi Echelon Vehicle Routing Problem with Service Times (HMSME-VRP-ST) is presented. This novel problem considers distribution applications in which goods are delivered either directly from a central depot or through intermediate facilities called switch points. Commodities are loaded into interchangeable containers called swap bodies at the central depot, and can be transferred from one vehicle to another at the switch points. The goal is to minimize fixed and variable costs of the vehicle fleet and swap -bodies while serving a predetermined set of customers. The routes are constrained by time and swap bodies by loading capacity. A mathematical model of the HMSME-VRP-ST is presented, and a Hybrid Clustered Ant Colony Optimization (HCACO) algorithm is proposed to address the complexity of the problem. The approach utilizes the ant -based clustering algorithm, combining the density and connectivity properties of clustering for creating promising neighborhoods with the solution construction methodology and pheromonebased memory of the Ant Colony Optimization framework. Additionally, two local search schemes based on Variable Neighborhood Descent are incorporated to further improve the generated solutions. The behavior of each HCACO variant is analyzed, and their results are compared to a Greedy Randomized Adaptive Search Procedure metaheuristic in 36 newly generated benchmarks comprising of clustered, uniformly random, and mixed clustered -random instances.

Optimal power flow (OPF) problem solution is a crucial task for the operators and decision makers to assign the best setting of the system components to obtain the most economic, environmental, and technical suitable state. Artificial Hummingbird Algorithm is a recent optimization algorithm that has been applied to solving several optimization problems. In this paper, a Modified Artificial Hummingbird Algorithm (MAHA) is proposed for improving the performance of the orignal Artificial Hummingbird Algorithm as well as effectivelly solve the OPF problem. The proposed MAHA is based on improving the searching capability by boosting the exploitation using the bandwidth motion around the best solution, while the exploration process is improved using the Levy flight distribution motion and the fitness-distance balance selection. This modified version helps overcome issues such as stagnation, premature convergence, and a propensity for local optima when tackling complex, nonlinear, and non-convex optimization problems like OPF. In order to confirm the effectiveness of the proposed algorithm, a series of tests are conducted on 23 standard benchmark functions, including CEC2020. The resulting outcomes are then compared to those obtained using other algorithms such as fitness-distance balance selection-based stochastic fractal search (FDBSFS), antlion optimizer (ALO), whale optimization algorithm (WOA), sine-cosine algorithm (SCA), fitness-distance balance and learning based artificial bee colony (FDB-TLABC), and traditional artificial hummingbird algorithm (AHA).The proposed algorithm is evaluated by solving the OPF problem with multiple objective functions on the IEEE 30-bus system. These objectives include fuel cost, fuel cost with valve loading effects, power losses, emissions, and voltage profile. Additionally, the algorithm's effectiveness is further assessed by testing it on single objective functions using medium and large-scale IEEE 57 and 118-bus networks.The results

In this paper, we investigate whether key relations in the crude oil market have been stable over time. This is done by estimating hybrid time-varying parameter structural Bayesian VAR models using monthly data ranging from February 1973 to May 2023. Model selection suggests that while stochastic volatility is preferred over homoscedasticity, the dynamics of the model are best described by constant parameters in all equations.

The Artificial Electric Field Algorithm (AEFA) stands out as a physics-inspired metaheuristic, drawing inspiration from Coulomb's law and electrostatic force; however, while AEFA has demonstrated efficacy, it can face challenges such as convergence issues and suboptimal solutions, especially in high-dimensional problems. To overcome these challenges, this paper introduces a modified version of AEFA, named mAEFA, which leverages the capabilities of Levy flights, simulated annealing, and the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms. While Levy flights enhance exploration potential and simulated annealing improves search exploitation, the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms are employed to add more diversity. The integration of these mechanisms in AEFA aims to expand its search space, enhance exploration potential, avoid local optima, and achieve improved performance, robustness, and a more equitable equilibrium between local intensification and global diversification. In this study, a comprehensive assessment of mAEFA is carried out, employing a combination of quantitative and qualitative measures, on a diverse range of 29 intricate CEC'17 constraint benchmarks that exhibit different characteristics. The practical compatibility of the proposed mAEFA is evaluated on five engineering benchmark problems derived from the civil, mechanical, and industrial engineering domains. Results from the mAEFA algorithm are compared with those from seven recently introduced metaheuristic algorithms using widely adopted statistical metrics. The mAEFA algorithm outperforms the LCA algorithm in all 29 CEC'17 test functions with 100% superiority and shows better results than SAO, GOA, CHIO, PSO, GSA, and AEFA in 96.6%, 96.6%, 93.1%, 86.2%, 82.8%, and 58.6% of test cases, respectively. In three out of five engineering design problems, mAEFA outperforms all the compared algorithms, securing second place in the remaining two prob

Planning for charging in transport missions is vital when commercial long-haul vehicles are to be electrified. In this planning, accurate range prediction is essential so the trucks reach their destinations as planned. The rolling resistance significantly influences truck energy consumption, often considered a simple constant or a function of vehicle speed only. This is, however, a gross simplification, especially as the tire temperature has a significant impact. At 80 km/h, a cold tire can have three times higher rolling resistance than a warm tire.A temperature-dependent rolling resistance model is proposed. The model is based on thermal networks for the temperature at four places around the tire. The model is tuned and validated using rolling resistance, tire shoulder, and tire apex temperature measurements with a truck in a climate wind tunnel with ambient temperatures ranging from -30 to 25 °C at an 80 km/h constant speed. Dynamic tire simulations were conducted using a heat transfer model, considering road, ambient, shoulder, and apex temperatures. The simulation results were compared with measured data for ambient, shoulder, and apex temperatures, and the model captures both time constants and stationary levels. The resulting model can predict the dynamics of the rolling resistance and will, therefore, give a more accurate prediction when tires are cold and warming up. Driving range simulations of a long haulage battery-electric truck have also been conducted demonstrating how the range changes with varying ambient temperatures as well as the influence a snapshot consumption has on range estimation., E-Charge

Manufacturing industries are eager to replace traditional robot manipulators with collaborative robots due to their cost-effectiveness, safety, smaller footprint and intuitive user interfaces. With industrial advancement, cobots are required to be more independent and intelligent to do more complex tasks in collaboration with humans. Therefore, to effectively detect the presence of humans/obstacles in the surroundings, cobots must use different sensing modalities, both internal and external. This paper presents a detailed review of sensor technologies used for detecting a human operator in the robotic manipulator environment. An overview of different sensors installed locations, the manipulator details and the main algorithms used to detect the human in the cobot workspace are presented. We summarize existing literature in three categories related to the environment for evaluating sensor performance: entirely simulated, partially simulated and hardware implementation focusing on the 'hardware implementation' category where the data and experimental environment are physical rather than virtual. We present how the sensor systems have been used in various use cases and scenarios to aid human-robot collaboration and discuss challenges for future work., Funding Agencies|Atlantic Technological University

Detailed longitudinal dynamics simulations may be used to predict the energy performance of road vehicles. However, including uncertainty in the operating conditions often implies high computational costs. Model-based formulations, in conjunction with statistical methods, may obviate this limitation by directly accounting for stochasticity, thus eliminating the need for simulating large populations of driving and operating cycles. To this end, leveraging directly the methods of stochastic calculus, this work presents a novel theory of longitudinal vehicle dynamics and energy consumption, where the vehicle’s speed varies stochastically depending on the characteristics of the operating environment. In particular, the proposed formulation, consisting of stochastic differential equations (SDEs) governing the longitudinal motion of road vehicles, inherently accounts for the statistical variation connected with uncertainties in the driver’s behavior and road properties, including, e.g., topography and legal speed. A Fokker-Planck partial differential equation (PDE) that describes the time evolution of the joint probability density function (PDF) of the vehicle’s speed, position, and road parameters is also derived from the SDEs established in the paper. The SDE and Fokker-Planck-based approaches enable statistical estimation of important quantities like speed fluctuations, instantaneous power requests, and energy consumption. The developed models may be used to assess the energy performance of road vehicles for different combinations of road transport missions. This is applicable at the early stages of the development, virtual testing, and certification processes, without the need to perform computationally expensive simulations, as corroborated by the virtual experiments conducted in the paper.

This paper studies the Hierarchical Multi -Switch Multi -Echelon VRP (HMSME-VRP), a newly introduced VRP variant based on a real -world case involving High Capacity Vehicles (HCV). The problem originates from the policies of a distribution company in the Nordic countries where HCVs of up to 34.5 m and up to 76 tons are allowed. The HMSME-VRP offer a new way to model distribution problems to cover large geographical areas without substantial costs in infrastructure. Furthermore, it adds complexity to the standard VRP and, as such, remains NP -hard and difficult to solve to optimality. Indeed, it has been demonstrated that only very small instances can be solved to optimality by a commercial solver. Thus, in order to handle instances of real -world size, we propose two General Variable Neighborhood Search (GVNS) procedures, the second of which is adaptive, utilizing an intelligent reordering mechanism. In order to evaluate the proposed procedures, 48 benchmark instances of various sizes and characteristics are generated and made publicly available, comprising of clustered, random, and semi -clustered customers. The computational results show that both GVNS procedures outperform the exact solver. Additionally, the adaptive version outperforms the conventional version based on both average and best solutions. Furthermore, we present a statistical analysis to verify the superiority of the adaptive version.

In this paper, we analyze the performance of a wireless caching system with heterogeneous traffic and relaying capabilities satisfying secrecy constraints for one of two receiving users. In this setup, the second user has no secrecy requirements and receives cacheable content either from the relay helper or the core network through a wireless base station. The wireless relay helper can assist both users since it is equipped with finite storage that is split into cacheable and non-cacheable storage. Concurrently, a passive eavesdropper tries to overhear transmissions to the user with secrecy requirements. Consequently, we examine how this relay’s storage split and the eavesdropper affect the performance of the average throughput and delay of the system as the transmission powers, the relay’s transmission probability, and the relay’s cache size vary. © 2023 IEEE., Cited by: 0; Conference name: 2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, ChileCon 2023; Conference date: 5 December 2023 through 7 December 2023; Conference code: 197291

Magnetic resonance imaging (MRI) is a slow diagnostic technique due to its time-consuming acquisition speed. To address this, parallel imaging and compressed sensing methods were developed. Parallel imaging acquires multiple anatomy views simultaneously, while compressed sensing acquires fewer samples than traditional methods. However, reconstructing images from undersampled multi-coil data remains challenging. Existing methods concatenate input slices and adjacent slices along the channel dimension to gather more information for MRI reconstruction. Implicit feature alignment within adjacent slices is crucial for optimal reconstruction performance. Hence, we propose MFormer: an accelerated MRI reconstruction transformer with cascading MFormer blocks containing multi-scale Dynamic Deformable Swin Transformer (DST) modules. Unlike other methods, our DST modules implicitly align adjacent slice features using dynamic deformable convolution and extract local non-local features before merging information. We adapt input variations by aggregating deformable convolution kernel weights and biases through a dynamic weight predictor. Extensive experiments on Stanford2D, Stanford3D, and large-scale FastMRI datasets show the merits of our contributions, achieving state-of-the-art MRI reconstruction performance. Our code and models are available at https://github.com/wafaAlghallabi/MFomer.

Transition metal nitride alloys possess exceptional properties, making them suitable for cutting applications due to their inherent hardness or as protective coatings due to corrosion resistance. However, the computational demands associated with predicting these properties using ab initio methods can often be prohibitively high at the conditions of their operation at cutting tools, that is, at high temperatures and stresses. Machine learning approaches have been introduced into the field of materials modeling to address the challenge. In this paper, we present an active learning workflow to model the properties of our benchmark alloy system cubic B1 Ti0.5Al0.5N at temperatures up to 1500 K. With a minimal requirement of prior knowledge about the alloy system for our workflow, we train a moment tensor potential (MTP) to accurately model the material's behavior over the entire temperature range and extract elastic and vibrational properties. The outstanding accuracy of MTPs with relatively little training data demonstrates that the presented approach is highly efficient and requires about two orders of magnitude less computational resources than state-of-the-art ab initio molecular dynamics., Funding Agencies|VINNOVA Excellence Center Functional Nanoscale Materials (FunMat-II) [2022-03071]; Knut and Alice Wallenberg Foundation (Wallenberg Scholar Grant) [KAW-2018.0194]; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoeping University [2009 00971]; Swedish Government Strategic Research Area in the Swedish e-Science Research Centre (SeRC); Swedish National Infrastructure for Supercomputing (SNIC); CSC IT Center for Science in Finland; National Academic Infrastructure for Supercomputing in Sweden (NAISS)

The process of data classification involves determining the optimal number of features that lead to high accuracy. However, feature selection (FS) is a complex task that necessitates robust metaheuristics due to its challenging NP-hard nature. This paper introduces a hybrid algorithm that combines the Artificial Ecosystem Optimization (AEO) operators with the Whale Optimization Algorithm (WOA) to enhance numerical optimization and FS. While the WOA algorithm, inspired by the hunting behavior of whales, has been successful in solving various optimization problems, it can sometimes be limited in its ability to explore and may become trapped in local optima. To address this limitation, the authors propose the use of AEO operators to improve the exploration process of the WOA algorithm. The authors conducted experiments to evaluate the effectiveness of their proposed method, called AEOWOA, using the CEC'20 test suite for numerical optimization and sixteen datasets for FS. They compared the results with those obtained from other optimization methods. Through experimental and statistical analyses, it was observed that AEOWOA delivers efficient search results with faster convergence, reducing the feature size by up to 89% while achieving up to 94% accuracy. These findings shed light on potential future research directions in this field.

A method for the approximate merging of disk Wang-Ball (DWB) curves based on the modified snake optimizer (BEESO) is proposed in this paper to address the problem of difficulties in the merging of DWB curves. By extending the approximate merging problem for traditional curves to disk curves and viewing it as an optimization problem, an approximate merging model is established to minimize the merging error through an error formulation. Considering the complexity of the model built, a BEESO with better convergence accuracy and convergence speed is introduced, which combines the snake optimizer (SO) and three strategies including bi-directional search, evolutionary population dynamics, and elite opposition-based learning. The merging results and merging errors of numerical examples demonstrate that BEESO is effective in solving approximate merging models, and it provides a new method for the compression and transfer of product shape data in Computer-Aided Geometric Design., Funding Agencies|National Natural Science Foundation of China

Medical image segmentation is crucial in using digital images for disease diagnosis, particularly in post-processing tasks such as analysis and disease identification. Segmentation of magnetic resonance imaging (MRI) and computed tomography images pose distinctive challenges attributed to factors such as inadequate illumination during the image acquisition process. Multilevel thresholding is a widely adopted method for image segmentation due to its effectiveness and ease of implementation. However, the primary challenge lies in selecting the optimal set of thresholds to achieve accurate segmentation. While Otsu's between-class variance and Kapur's entropy assist in identifying optimal thresholds, their application to cases requiring more than two thresholds can be computationally intensive. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. In this paper, we introduce an improved chameleon swarm algorithm (ICSA) to address these limitations. ICSA is designed for image segmentation and global optimization tasks, aiming to improve the precision and efficiency of threshold selection in medical image segmentation. ICSA introduces the concept of the "best random mutation strategy" to enhance the search capabilities of the standard chameleon swarm algorithm (CSA). This strategy leverages three distribution functions-Levy, Gaussian, and Cauchy-for mutating search individuals. These diverse distributions contribute to improved solution quality and help prevent premature convergence. We conduct comprehensive experiments using the IEEE CEC'20 complex optimization benchmark test suite to evaluate ICSA's performance. Additionally, we employ ICSA in image segmentation, utilizing Otsu's approach and Kapur's entropy as fitness functions to determine optimal threshold values for a set of MRI images. Comparative

In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO is a math-inspired optimizer that has many limitations in handling complex multi -modal problems. mEDO tries to solve these drawbacks using 2 operators: phasor operator for diversity enhancement and an adaptive p -best mutation strategy for preventing it converging to local optima. To validate the effectiveness of the suggested optimizer, a comprehensive set of comparative experiments using the CEC'2020 test suite was conducted. The experimental results consistently prove that the suggested technique outperforms its counterparts in terms of both convergence speed and accuracy. Moreover, the suggested mEDO algorithm was applied for image segmentation using the multi-threshold image segmentation method with Otsu's entropy, providing further evidence of its enhanced performance. The algorithm was evaluated by comparing its results with those of existing well-known algorithms at various threshold levels. The experimental results validate that the proposed mEDO algorithm attains exceptional segmentation results for various threshold levels., Funding Agencies|Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia [PNURSP2023R361]

We derived an explicit form of the Jacobian for discrete approximations of a nonlinear initial boundary value problems (IBVPs) in matrix-vector form. The Jacobian is used in Newton's method to solve the corresponding nonlinear system of equations. The technique was exemplified on the incompressible Navier-Stokes equations discretized using summation-by-parts (SBP) difference operators and weakly imposed boundary conditions using the simultaneous approximation term (SAT) technique. The convergence rate of the iterations is verified by using the method of manufactured solutions. The methodology in this paper can be used on any numerical discretization of IBVPs in matrix-vector form, and it is particularly straightforward for approximations in SBP-SAT form., Funding Agencies|Vetenskapsradet, Sweden [2021-05484 VR]; University of Johannesburg

With the purpose of analysing Swedish companies' adherence and adoption of the information security standard ISO 27001 and to examine the communicative constitution of preventive innovation in organisations, we have created a corpus of corporate texts from Swedish company websites. The corpus was analysed from multiple interdisciplinary perspectives in close cooperation with management researchers and SweClarin researchers using SweClarin tools and resources as well as standard language technology tools. Some analyses require deep reading, which was performed by management researchers, often guided by results from language analyses. Initial results have been presented at a management studies conference. In this paper, we focus on presenting the research issues, the methods used in the project, the results, and the experience of SweClarin researchers supporting researchers in social sciences. Our contribution is to show how it is possible, through the integration of human insights and digital methods, to increase the credibility and validity of a digitally acquired data set and subsequent research findings. In our view, a combination of human deep reading (management researchers), contextual lexical verification (management studies) and language technology (content and sentiment analysis) can help to sensitise computational text analysis for medium-sized data sets.

Efficient traffic management requires an understanding of mobility patterns in the road network, where one important component is route choice. This study aims to analyze how route choice models can be adapted to efficient urban traffic management and intelligent transport systems (ITS), by constructing route sets and attributes from GPS and network data. With a route choice model that is responsive to traveltime changes in the network, travel behavior during incidents can be predicted to evaluate traffic management policies, such as traveler information and traffic control. The dataset consists of about 400,000 vehicle trips and is divided into a training dataset and a test dataset. The two datasets are compared, and the experiments show that the routes used are similar. Discrete route choice models are estimated with one data-driven path identification approach (DDPI) and one where the data-driven path set is augmented with routes from a network-based shortest path generation with link penalty (NBPA). The result suggests that the traveltime has a larger impact on the route choice when the model is trained on the NBPA route set and that the route's simplicity, length, and traveltime are important attributes for the route choice, which are useful insights in a traffic management context., Funding: This work was supported by the Swedish Transport Administration (Trafikverket) via the Centre for Traffic Research (CTR) [grant number TRV 2020/118663].

Metabolic theories in ecology interpret ecological patterns at different levels (individuals, populations, communities) through the lens of metabolism, often applying allometric scaling with rates of energy use described as power functions of body mass. However, the application of metabolic theory at higher levels requires a sound theory for metabolism at the individual level. In this thesis, metabolic theory has been developed and applied in three different contexts; 1) growth of individual organisms under food limitation, 2) life-history theory for age and size at maturity for individual organisms, and 3) population growth of marine mammals exposed to bioaccumulative toxicants through their diet. In the first context, a new mechanistic model for individual growth was developed, based on an energy balance that expresses growth as the net result of energy assimilation from food and various metabolic costs. The model can account for effects of body composition and cellular-level growth patterns, but foremost it considers a potential trade-off between regulated maintenance and growth under food limitation. The model was successfully calibrated and validated against empirical data for an insect (house cricket) under both unlimited and limited food conditions. Interestingly, the empirical calibration indicated that the energy per unit body mass that an organism allocates to maintenance of body structures may increase as the organism grows and may also be upregulated under food limitation. In Paper I, the maintenance-growth model (MGM), is presented, derived and demonstrated via numerical simulations and comparisons with available growth data. In Paper II and III, MGM is calibrated and evaluated against collected data for house crickets growing under unlimited and restricted food supply, respectively. In the second context (Paper IV), it was investigated how models for individual growth and mortality can be combined with life-history theory to model plastic responses in, Funding agencies: The Swedish research council, grant number 2018-05523, Viltforskningsanslaget, Swedish Environmental Protection Agency, and the BONUS program BaltHealth (Art. 185).

As one of the major causes of antimicrobial resistance, beta-lactamase develops rapidly among bacteria. Detection of beta-lactamase in an efficient and low-cost point-of-care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)-assisted mobile health system that consists of a paper-based beta-lactamase fluorogenic probe analytical device and a smartphone-based AI cloud. An ultrafast broad-spectrum fluorogenic probe (B1) that could respond to beta-lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three-dimensional microfluidic paper-based analytical device was fabricated for integration of B1. Also, a smartphone-based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the beta-lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem-solving ability in interdisciplinary research, and demonstrates potential clinical benefits., Funding Agencies|National Key R&D Program of China [2020YFA0709900]; National Natural Science Foundation of China [62288102, 22077101, 52073230]; Joint Research Funds of Department of Science & Technology of Shaanxi Province; Northwestern~Polytechnical University [2020GXLH-Z-008, 2020GXLH-Z-013]; Shaanxi Provincial Science Fund for Distinguished Young Scholars [2023-JC-JQ-32]; Key Research and Development Program of Shaanxi [2020ZDLGY13-04]; Fundamental Research Funds for the Central Universities and Innovation Foundation for Doctorate Dissertation of Northwestern Polytechnical University [CX2021121]

Remanufacturing has been modelled extensively from an economic perspective. However, there are relatively few studies focusing on models that consider the implementation difficulties in industry, such as data availability and model transparency. This paper aims to identify how remanufacturing initiations can be economically assessed with models particularly suitable for industry. These models should contribute to disseminating the know-how about initiating remanufacturing to transit industries towards more circular product flows. To fulfil this aim, a systematic literature review was conducted. The search resulted in a total of 964 papers published till August 2021. The results show that assessment models could be categorised into a two-dimensional framework indicating the level of computer support needed to handle the models and the level of data requirements to use the models, both of which are considered essential in the industrial implementation of the models. The review revealed and structured a further need for research on assessing remanufacturing initiation profitability, especially on economic assessment models designed to assist industries without previous remanufacturing knowledge in such an initiation., Remometer, Mistra REES (Resource-Efficient and Effective Solutions)

Begreppet kritiskt tänkande har inte en enhetlig definition fast kognitiva färdigheter så som att kunna ställa adekvata frågor, utvärdera, analysera, reflektera och dra slutsatser brukar höra till (Hedin, 2006). I examensfordringarna för civilingenjörsexamen (Högskoleförordningen, 1993) ingår att studenterna ska visa förmåga att ”kritiskt, självständigt och kreativt identifiera, formulera och hantera komplexa frågeställningar”, samt ”planera och med adekvata metoder genomföra kvalificerade uppgifter inom givna ramar”. Det krävs alltså att studenterna, med lärarnas hjälp, utvecklar ett flertal avancerade förmågor kopplat till kritiskt tänkande. Vidare bedrivs ingenjörsutbildningarna vid LiU enligt CDIO-ramverket (Crawley et. al., 2014), vilket inkluderar förmågor att definiera systemgränser, abstrahera, förstå helheter och delsystem, samt göra prioriteringar och avvägningar (avdelning 2.3 i CDIO Syllabus). I Kursen Logistikprojekt (12hp, A-nivå) för civilingenjörsstudenter termin 9 gör studenterna gruppvis logistikutredningar med var sin extern organisation som uppdragsgivare. Med hjälp av såväl litteratur som information från uppdragsgivaren ska undersökningsfrågor preciseras, och metoder för datainsamling och analys väljas. Utmaningarna kopplar an till de mest avancerade nivåerna i Blooms taxonomi (Krathwohl, 2002) och SOLO-taxonomin (Biggs et al., 2022). Kritiskt tänkande innefattar en rad mentala processer och färdigheter som t.ex tolkning, analys, utvärdering, slutledning, förklaring och självreglering (Alsaleh, 2020). Studenterna får i kursen alltså möjlighet att utveckla och träna på dessa färdigheter som bygger förmågan kritiskt tänkande. Studenterna upplever dock vissa svårigheter att genomföra utredning på ett strukturerat sätt som bäddar för trovärdiga och underbyggda resultat. I syfte att som lärare bättre kunna bistå studenterna adresseras därför följande frågor: Vilka svårigheter upplever studenterna när de ska lära sig utredningsmetodik? Hur ka, PUG 2023

Exploration in dynamic and uncertain real-world environments is an open problem in robotics and it constitutes a foundational capability of autonomous systems operating in most of the real-world. While 3D exploration planning has been extensively studied, the environments are assumed static or only reactive collision avoidance is carried out. We propose a novel approach to not only avoid dynamic obstacles but also include them in the plan itself, to deliberately exploit the dynamic environment in the agent's favor. The proposed planner, Dynamic AutonomousExploration Planner (DAEP), extends AEP [1] to explicitly plan with respect to dynamic obstacles. Furthermore, addressing prior errors within AEP in DAEP has resulted in enhanced exploration within static environments. To thoroughly evaluate exploration planners in dynamic settings, we propose a new enhanced benchmark suite with several dynamic environments, including large-scale outdoor environments. DAEP outperforms state-of-the-art planners in dynamic and large-scale environments and is shown to be more effective at both exploration and collision avoidance.

In different sports fields, collecting play-by-play data has become significant for data analysis, as seen in sports like baseball, basketball, ice hockey and football. Badminton remains a relatively new domain in terms of systematically collecting game play data (i.e., play-by-play or shot-by-shot) with only some recently published datasets online. While the game play data provides some detailed information about matches, it lacks rich semantics for complex information retrieval and data analysis. Consequently, the data cannot be used for applications where semantics are needed. This paper introduces a badminton domain ontology - BadmintONTO, along with its illustrative usages to showcase the capability of this ontology to represent basic domain knowledge and to annotate play-by-play data.

The field of visual object tracking is dominated by methods that combine simple tracking algorithms and ad hoc schemes. Probabilistic tracking algorithms, which are leading in other fields, are surprisingly absent from the leaderboards. We found that accounting for distance in target kinematics, exploiting detector confidence and modelling non-uniform clutter characteristics is critical for a probabilistic tracker to work in visual tracking. Previous probabilistic methods fail to address most or all these aspects, which we believe is why they fall so far behind current state-of-the-art (SOTA) methods (there are no probabilistic trackers in the MOT17 top 100). To rekindle progress among probabilistic approaches, we propose a set of pragmatic models addressing these challenges, and demonstrate how they can be incorporated into a probabilistic framework. We present BASE (Bayesian Approximation Single-hypothesis Estimator), a simple, performant and easily extendible visual tracker, achieving state-of-the-art (SOTA) on MOT17 and MOT20, without using Re-Id. Code available at https://github.com/ffi-no/paper-base-visapp-2024.

In distributed massive multiple-input multiple-output (MIMO) systems, multiple geographically separated access points (APs) communicate simultaneously with a user, leveraging the benefits of multi-antenna coherent MIMO processing and macro-diversity gains from the distributed setups. However, time and frequency synchronization of the multiple APs is crucial to achieve good performance and enable joint precoding. In this paper, we analyze the synchronization requirement among multiple APs from a reciprocity perspective, taking into account the multiplicative impairments caused by mismatches in radio frequency (RF) hardware. We demonstrate that a phase calibration of reciprocity-calibrated APs is sufficient for the joint coherent transmission of data to the user. To achieve synchronization, we propose a novel over-the-air synchronization protocol, named BeamSync, to calibrate the geographically separated APs without sending any measurements to the central processing unit (CPU) through fronthaul. We show that sending the synchronization signal in the dominant direction of the channel between APs is optimal. Additionally, we derive the optimal phase and frequency offset estimators. Simulation results indicate that the proposed BeamSync method enhances performance by 3 dB when the number of antennas at the APs is doubled. Moreover, the method performs well compared to traditional beamforming techniques.

This study examines the transition from linear to circular packaging, highlighting the role of smart packaging technologies in enhancing circular economy strategies. We explore the challenges and technological facilitators in adopting circular packaging through an embedded case study approach involving Scandinavian manufacturers. Our findings suggest that smart packaging, equipped with sensors and digital capabilities, helps address supply chain complexities and regulatory compliance issues. It also has the potential to improve consumer acceptance and foster collaboration among stakeholders. The paper underscores the potential of integrating smart technologies into packaging to accelerate sustainable practices within the CE framework.

Purpose – This paper aims to further our understanding of policy–practice gaps in organizations from an organizational learning perspective. The authors conceptualize and analyze policy–practice gaps in terms of what they label the dual challenge of organizational learning, i.e. the organizational tasks of both adapting ongoing practices to prescribed policy demands and adapting the policy itself to the needs of practice. Specifically, the authors address how this dual challenge can be understood in terms of organizational learning and how an organization can be managed to successfully resolve the dual learning challenge and, thereby, bridge policy–practice gaps in organizations. Design/methodology/approach – This paper draws on existing literature to explore the gap between policy and practice. Through a synthesis of theories and an illustrative practical example, this paper highlights key conceptual underpinnings. Findings – In the analysis of the dual challenge of organizational learning, this study provides a conceptual framework that emphasizes the important role of tensions and contradictions between policy and practice and their role as drivers of organizational learning. To bridge policy–practice gaps in organizations, this paper proposes five key principles that aim to resolve the dual challenge and accommodate both deployment and discovery in organizations. Research limitations/implications – Because this is a conceptual study, empirical research is called for to explore further and test the findings and conclusions of the study. Several avenues of possible future research are proposed. Originality/value – This paper primarily contributes by introducing and elaborating on a conceptual framework that offers novel perspectives on the dual challenges of facilitating both discovery and deployment processes within organizations.

Enterprises are facing the superimposed challenges of digitalization and greening. The shift from reactive green technology innovation (RGT) to proactive green technology innovation (PGT) has special significance for sustainable economic development. Which strategies will companies choose? Can digital transformation (DT) motivate companies to transform their green innovation strategies? Enterprises' green innovation strategy choices must be explained with regard to digitalization. The purpose of this paper is to reveal how digitalization affects the choice of green innovation strategies and to provide a realistic basis for the sustainable development of heavily polluting enterprises. We formulated a "DT -capability -strategy" theoretical framework incorporating external constraints and internal attitudes to empirically test the microdata of 505 heavily polluting enterprises. The results show that: (1) DT can shift the heavily polluting enterprises' green innovation strategies from RGT to PGT. Endogenous tests and robustness tests support this conclusion. (2) A mechanism test shows that environmental regulations cannot significantly regulate a green innovation strategy. Only a company's capabilities and attitudes can influence PGT but their effects on RGT are not statistically significant. (3) The influence of DT on green innovation strategies shows multi -dimensional heterogeneity in the digital infrastructure, scale, and innovation level of the enterprise. The conclusions provide implications for enterprises to integrate their digital and green behaviors., Funding Agencies|Philosophy and Social Science Foundation of China [22AJY005]; The 2023 Xinjiang Production and Construction Corps (XPCC) Graduate Innovation Project

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system.

In search of more efficient hydraulic systems, new system architectures are explored. These system architectures are often electrically driven and include energy recuperation. This requires hydraulic machines to function both as pumps, converting mechanical power into hydraulic power, and as motors, converting hydraulic power back into mechanical power. However, the availability of machines that can operate in all desired modes is limited. This indicates that operation in multiple modes comes with performance penalties. This paper highlights the challenges for multi-quadrant operation of hydraulic piston pump/motors, with a particular focus on commutation, i.e., the transition between high- and low-pressure level for each chamber. Various commutation strategies for piston machines are examined. Furthermore, other important aspects for pump/motor operation such as hydrostatic compensation ratios, design of inlet channels, low-speed capability, and flow control through speed or displacement control are discussed. The article shows that the design of multi-quadrant machines is challenging, and this has to be considered when choosing the system architecture., Funding Agencies|Swedish Electromobility Centre [13070]; Swedish Energy Agency [P2023-00594]

Global labor market shifts have spurred the need for innovations and adaptations in workplace norms. This evolution demands a workforce with technical and soft skills to meet sustainability and industry advancements. The paper aims to elucidate the complex challenges related to the ambition to develop a socially sustainable, competitive, and green industry subjected to an accelerating pace of change. It outlines the findings of a Delphi study conducted in Sweden, which integrated workshops, interviews, and surveys with experts from various sectors to identify 14 key challenges. These challenges were synthesized into five themes: innovative competence supply management practices, resilient organizations and production systems, analytics for improvement and learning, socially sustainable work, and green transformation practices. The study provides a set of propositions within these themes, offering a strategic roadmap for future research to foster the growth of industries that are socially responsible, competitive, and committed to environmental sustainability. A practical implication of the study is the recognition of the larger competence ecosystem of which industrial companies are a part. This community must work together to create the knowledge needed to manage the shift to a green, sustainable, and digital working life., Funding Agencies|Vinnova, Sweden's innovation agency; XPRES (Initiative for Excellence in Production Research)

The construction industry is the largest contributor of waste in the EU, and research shows that a high sorting rate does not automatically entail a high recycling rate. The purpose of this study is, through a multiple case study, to provide better understanding of the waste flows and the prerequisites on the logistical operations within new build construction projects, considering the variation over time (construction phases). Waste data from five projects were analyzed. Results show that amount and mix of waste varieties vary considerably during a project, which has to be effectively managed by the waste logistics system., BÖRjA

Purpose: There is an overarching need to increase circularity in the construction sector, but not much is happening and only 1% of aggregate materials are being reused. Different construction actors can impact circularity, but their drivers and barriers are still unknown. Therefore, the purpose of this study is to explore how different drivers and barriers impactthe circular use of aggregate materials. Design/methodology/approach: The purpose of this research was achieved using a qualitative approach based on three workshops and four interviews in Östergötland, Sweden. Findings: The study found that drivers and barriers of circular aggregate materials can be thematically grouped into eight themes. Based on those findings, we propose a three-step model to increase circularity of aggregates; 1. Plan for mass handling spaces in the strategic municipalplans; 2. Develop common definitions of circular aggregate materials that corresponds tothose of virgin materials; 3. Develop and introduce (digital) information systems to increase the capacity of matching aggregates. Research limitations/implications: This study was based on workshops and interviews with established organizations within the Östergötland region. Future research should include other regions, and the view of the local small construction firms or mass handlers etc. Practical implications: This study helps organizations to understand how they can work towards circular aggregate materials in collaboration with other organizations in a structured way. Original/value: This study is one of the first in-depth studies that investigates drivers and barriers of circular reuse of aggregate materials in construction.

Companies must enhance total maintenance effectiveness to staycompetitive, focusing on both digitalization and basic maintenance procedures.Digitalization offers technologies for data-driven decision-making, but manymaintenance decisions still lack a factual basis. Prioritizing efficiency andeffectiveness require analyzing equipment history, facilitated by usingComputerized Maintenance Management Systems (CMMS). However, CMMS dataoften contains unstructured free-text, leading to manual analysis, which is resourceintensiveand reactive, focusing on short time periods and specific equipment. Twoapproaches are available to solve the issue: minimizing free-text entries or usingadvanced methods for processing them. Free-text allows detailed descriptions butmay lack completeness, while structured reporting aids automated analysis but maylimit fault description richness. As knowledge and experience are vital assets forcompanies this research uses a hybrid approach by combining Natural LanguageProcessing with domain specific ontology and Large Language Models to extractinformation from free-text entries, enabling the possibility of real-time analysis e.g.,identifying recurring failure and knowledge sharing across global sites.

The 5th-Generation New Radio (5G-NR) network have been widely deployed around the world in the frequency range 1/range 2 bands. Once specific frequency bands within these ranges can vary across different countries and regions due to regulatory differences, it should be carried out radio network planning to evaluate the 5G coverage considering the particularities of different locations. In this regard, this paper performs a throughly analysis of the following methods for modeling wireless channel propagation in Quito, Ecuador: 3rd Generation Partnership Project, Knife Edge Diffraction (KED), ASTER and Dominant Path model (DPM). Specifically, we focus on KED, ASTER, and DPM for 3.5/28-GHz bands to determine the propagation models in three-Dimensional urban macro scenarios. In the radio network planning, the multiple-input multiple-output array antennas, 2x2/4x4 configuration radiation patterns are deployed using WINPROP tool and 64 x 64 array configuration with the ATOLL tool. 5G frequency specifications, path loss, influence of diffraction, reflection, blocking, and fading between transmitter and receiver have been considered for scenarios of interest, such as dense urban and urban in Quito, by using fixed wireless access applications and Vehicular-to-Everything (V2X) communications. In addition, data rates, throughput, and the quality metrics of the received reference signal, i.e., the signal-to-noise plus interference ratio, the reference signal received quality, the reference signal received power, and the received signal strength indicator, are also assessed for each propagation model. Finally, we provide useful insights into propagation models and design usage rules for the bands mentioned in 5G networks for Quito city., Funding Agencies|Excellence Center at Linkping - Lund in Information Technology (ELLIIT)

Existing safety assurance approaches for autonomous vehicles (AVs) perform system-level safety evaluation by placing the AV-under-test in challenging traffic scenarios captured by abstract scenario specifications and investigated in realistic traffic simulators. As a first step towards scenario-based testing of AVs, the initial scene of a traffic scenario must be concretized. In this context, the scene concretization challenge takes as input a high-level specification of abstract traffic scenes and aims to map them to concrete scenes where exact numeric initial values are defined for each attribute of a vehicle (e.g. position or velocity). In this paper, we propose a traffic scene concretization approach that places vehicles on realistic road maps such that they satisfy an extensible set of abstract constraints defined by an expressive scene specification language which also supports static detection of inconsistencies. Then, abstract constraints are mapped to corresponding numeric constraints, which are solved by metaheuristic search with customizable objective functions and constraint aggregation strategies. We conduct a series of experiments over three realistic road maps to compare eight configurations of our approach with three variations of the state-of-the-art Scenic tool, and to evaluate its scalability., Funding Agencies|NSERC

One approach to reduce sedentary behavior isthrough casual exergames, exergames with simplegame mechanics played during short time increments.Casual exergames have shown success in producingenjoyable experiences and sufficient levels of exertion.Yet, previous research has neglected what to considerin order to design enjoyable and exerting experiencesin casual exergames. The present work explores thisaspect through an exploratory mixed methodsresearch design, comparing the perceived exertionand enjoyment of four different casual exergames. Inrelation to traditional exergame guidelines werecommend that casual exergames focus on allowingfor high ambiguity of movements, balance thecognitive complexity of the game, and direct players’attention from the body by utilizing fast game pace.

In this paper, the high-temperature constitutive behaviour of an additively manufactured ductile nickel-based superalloy is investigated and modelled, with application to thermomechanical fatigue, low-cycle fatigue and creep conditions at temperatures up to 800∘800∘C. Thermomechanical fatigue tests have been performed on smooth specimens in both in-phase and out-of-phase conditions at a temperature range of 100−800∘100−800∘C, and creep tests at 625∘625∘C, 700∘700∘C, 750∘750∘C and 800∘800∘C. Additionally, low-cycle fatigue tests at different strain ranges and load ratios have been performed at 700∘700, Funding Agencies|Linkoping University and Siemens Energy AB

Making information accessible to diverse target audiences, including individuals with dyslexia and cognitive disabilities, is crucial. Automatic Text Simplification (ATS) systems aim to facilitate readability and comprehension by reducing linguistic complexity. However, they often lack customizability to specific user needs, and training data for smaller languages can be scarce. This paper addresses ATS in a Swedish context, using methods that provide more control over the simplification. A dataset of Swedish paraphrases is mined from large amounts of text and used to train ATS models utilizing prefix-tuning with control prefixes. We also introduce a novel data-driven method for selecting complexity attributes for controlling the simplification and compare it with previous approaches. Evaluation of the trained models using SARI and BLEU demonstrates significant improvements over the baseline -- a fine-tuned Swedish BART model -- and compared to previous Swedish ATS results. These findings highlight the effectiveness of employing paraphrase data in conjunction with controllable generation mechanisms for simplification. Additionally, the set of explored attributes yields similar results compared to previously used attributes, indicating their ability to capture important simplification aspects.

Nonlinear degradation prediction of lithium -ion batteries is of great value in battery management systems. Knee point in degradation is a significant signal for state of health. However, large data generated by diverse aging mechanisms and dynamic operating conditions cannot be used completely in prediction. Firstly, this paper proposes a novel data -driven discrete grey model for battery degradation with knee point based on small sample modeling. Secondly, combined with four non -numerical differential methods, knee point is identified according to the predicted degradation trajectory. Then, batteries in 24 different fast charging modes are selected as a set of experimental subjects. Results show that 40% data of total cycles is sufficient to effectively predict battery degradation trajectory and identify the knee point. Finally, performance of fast charging modes is analyzed based on the predicted knee point. This work demonstrates the strong applicability of the grey model in reasonably predicting battery degradation trajectory and accurately identifying the knee point, providing a reference for fast charging schemes in complex engineering systems., Funding Agencies|National Natural Science Foundation of China [72171116]; Fundamental Research Funds for the Central Universities [NZ2020022]; The 333 talent project in Jiangsu Province (China)

Keypoint detection is a pivotal step in 3D reconstruction, whereby sets of (up to) K points are detected in each view of a scene. Crucially, the detected points need to be consistent between views, i.e., correspond to the same 3D point in the scene. One of the main challenges with keypoint detection is the formulation of the learning objective. Previous learning-based methods typically jointly learn descriptors with keypoints, and treat the keypoint detection as a binary classification task on mutual nearest neighbours. However, basing keypoint detection on descriptor nearest neighbours is a proxy task, which is not guaranteed to produce 3D-consistent keypoints. Furthermore, this ties the keypoints to a specific descriptor, complicating downstream usage. In this work, we instead learn keypoints directly from 3D consistency. To this end, we train the detector to detect tracks from large-scale SfM. As these points are often overly sparse, we derive a semi-supervised two-view detection objective to expand this set to a desired number of detections. To train a descriptor, we maximize the mutual nearest neighbour objective over the keypoints with a separate network. Results show that our approach, DeDoDe, achieves significant gains on multiple geometry benchmarks. Code is provided at http://github.com/Parskatt/DeDoDegithub.com/Parskatt/DeDoDe.

In this paper, we investigate the benefits of hardware-aware quantization in the gFADES hardware accelerator targeting Graph Convolutional Networks (GCNs). GCNs are a type of Graph Neural Networks (GNNs) that combine sparse and dense data compute requirements that are challenging to meet in resource-constrained embedded hardware. The gFADES architecture is optimized to work with the pruned data representations typically present in graph neural networks for the graph structure and features. It is described in High-Level Synthesis (HLS) which enables efficient design-space exploration of mixed precision hardware configurations. In this work, the mixed-precision design is embedded in the forward pass of the PyTorch back-propagation training loop to enable run-time hardware-aware training. It uses different data types to represent adjacency, feature, weight, internal, and output values which allows for a fine-grained optimization at the tensor level. The resulting hardware configuration after training reduces precision to a 4-bit data type for all inputs. It achieves little to no degradation in the classification accuracy, when training on the Planetoid database dataset, compared to the original 32-bit floating-point. The optimized hardware design running on an AMD/Xilinx Zynq Ultrascale+ FPGA device achieves over 600x speedup compared to the optimized PyTorch software implementation running on the multi-core ARM CPU in the processing system., Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Ports are actively pursuing greater operational efficiency to effectively handle the increasing global flow of goods, while striving to improve the energy efficiency of their operations to comply with new environmental regulations. As a result, innovation-leading ports have begun to recognize the potential of digital twins to overview, coordinate and optimize port processes, resulting in energy savings, and reductions of costs and of CO2 emissions. While digital twins have gained momentum in other domains such as smart manufacturing and aerospace, their adoption in ports has been comparatively slow. This can be explained, among other things, by the multi-stakeholder nature of the port and the high complexity of the often interconnected port processes. Thus, this thesis, grounded in the context of ports, discusses what constitutes a digital twin, proposes characteristics to assess the maturity of existing digital twins, and introduces and evaluates mathematical models to support a key port process, which can be used as components of a digital twin for the port. The thesis is composed of three papers: Paper 1 is based on an extensive literature review, through which digital twins among different domains are studied in depth in order to transfer insights from these to the port domain. The resulting discussion of what constitutes a port’s digital twin and the requirements that a port’s digital twin must fulfil, together with a discussion of use cases of how port digital twins can contribute to energy savings, form the basis of Paper 1. Paper 2 discusses how digital twins’ maturity can be assessed within six maturity levels and presents milestones for their implementation. Notably, Interoperability is identified as the highest maturity level, as the numerous stakeholders and their respective digital twins must work together to reach a coordinated system of systems performance. Using this assessment demonstrates that only a few innovation-leading ports have developed so, Hamnar strävar aktivt efter ökad operativ effektivitet för att hantera den ökande globala varuflödet, samtidigt som de strävar efter att förbättra energieffektiviteten. Som ett resultat har ledande hamnar börjat se potentialen hos digitala tvillingar för att skapa överblick samt koordinera och optimera processer i hamnen. Målet med användningen av digitala tvillingar är energibesparingar samt minskning av kostnader och CO2-utsläpp. Medan digitala tvillingar har använts inom andra områden såsom tillverknings-, flyg- och rymdindustrin, har införandet i hamnar varit jämförelsevist långsamt. Detta kan förklaras, bland annat, av hamnens många olika involverade aktörer och den höga komplexiteten i de ofta sammanlänkade hamnprocesserna. Därför fokuserar denna avhandling, med utgångspunkt i hamnkontexten, vad som utgör en digital tvilling, presenterar egenskaper för olika mognadsnivåer hos befintliga digitala tvillingar, och introducerar samt utvärderar matematiska modeller som kan bli delkomponenter i en digital tvilling för hamnen. Avhandlingen består av tre artiklar: Artikel 1 bygger på en omfattande litteraturöversikt, inom vilken digitala tvillingar för olika områden studeras ingående för att överföra insikter från dessa till hamndomänen. Detta resulterar i en presentation av vad som utgör en hamns digitala tvilling och de krav som en hamns digitala tvilling måste uppfylla, tillsammans med en diskussion om möjliga sett på vilka hur hamnens digitala tvillingar kan bidra till energibesparingar. Artikel 2 presenterar ett ramverk för hur mognaden hos digitala tvillingar kan bedömas baserat på sex mognadsnivåer och presenterar milstolpar för deras implementering. Noterbart är att interoperabilitet identifieras som den högsta mognadsnivån, eftersom de många intressenterna och deras respektive digitala tvillingar måste koordineras för att nå en fungerande system-av-systemnviå. Genom att använda denna bedömning visar det sig att endast några få innovationsledande hamnar hittil

Backscatter communication (BC) is a promising technique for future Internet-of-Things (IoT) owing to its low complexity, low cost, and potential for energy-efficient operation in sensor networks. There are several network infrastructure setups that can be used for BC with IoT nodes. One of them is the bistatic setup where typically there is a need for high dynamic range and high-resolution analog-to-digital converters at the reader. In this paper, we investigate a bistatic BC setup with multiple antennas. We propose a novel transmission scheme, which includes a protocol for channel estimation at the carrier emitter (CE) as well as a transmit beamformer construction that suppresses the direct link interference between the two ends of a bistatic link (namely CE and reader), and increases the detection performance of the backscatter device (BD) symbol. Further, we derive a generalized log-likelihood ratio test (GLRT) to detect the symbol/presence of the BD. We also provide an iterative algorithm to estimate the unknown parameters in the GLRT. Finally, simulation results show that the required dynamic range of the system is significantly decreased, and the detection performance of the BD symbol is increased, by the proposed algorithm compared to a system not using beamforming at the CE., Funding Agencies|REINDEER Project of the European Union's Horizon 2020 Research and Innovation Program

We introduce discriminator guidance in the setting of Autoregressive Diffusion Models. The use of a discriminator to guide a diffusion process has previously been used for continuous diffusion models, and in this work we derive ways of using a discriminator together with a pretrained generative model in the discrete case. First, we show that using an optimal discriminator will correct the pretrained model and enable exact sampling from the underlying data distribution. Second, to account for the realistic scenario of using a sub-optimal discriminator, we derive a sequential Monte Carlo algorithm which iteratively takes the predictions from the discriminator into account during the generation process. We test these approaches on the task of generating molecular graphs and show how the discriminator improves the generative performance over using only the pretrained model., This article has a CC BY-licence.