The agricultural sector faces persistent threats from plant diseases and pests, with Tuta absoluta posing a severe risk to tomato farming by causing up to 100% crop loss. Timely pest detection is essential for effective intervention, yet traditional methods remain labor-intensive and inefficient. Recent advancements in deep learning offer promising solutions, with YOLOv8 emerging as a leading real-time detection model due to its speed and accuracy, outperforming previous models in on-field deployment. This study focuses on the early detection of Tuta absoluta-induced tomato leaf diseases in Sub-Saharan Africa. The first major contribution is the annotation of a dataset (TomatoEbola), which consists of 326 images and 784 annotations collected from three different farms and is now publicly available. The second key contribution is the proposal of a transfer learning-based approach to evaluate YOLOv8’s performance in detecting Tuta absoluta. Experimental results highlight the model’s effectiveness, with a mean average precision of up to 0.737, outperforming other state-of-the-art methods that achieve less than 0.69, demonstrating its capability for real-world deployment. These findings suggest that AI-driven solutions like YOLOv8 could play a pivotal role in reducing agricultural losses and enhancing food security. 

Studiens syfte och betydelse

Ekonomisk tillväxt är ett centralt mål i ekonomisk politik. En viktig källa till tillväxt är ökad produktivitet. Eftersom uppföljning av produktivitet i Sverige bygger på föråldrade eller partiella metoder, har denna studie haft som syfte att använda moderna metoder för att mäta totalfaktorproduktivitet (TFP). TFP ger en mer heltäckande bild av produktivitet än enklare mått som arbetsproduktivitet (AP). En fördel med den mer moderna metoden för att mäta produktivitet är att de kan delas upp i drivkrafterna för desamma. Detta gör det möjligt att på ett mer precist sätt inrikta policyrekommendationer och åtgärder. 

The paper presents a replicable simulation architecture to assess the economic, environmental, and resilience performance of commercial electric and automated marine passenger transport systems. Built on Discrete Event Simulation (DES), the architecture balances simulation complexity and detail in early system development. It comprises key components such as the modelling framework, data flow, data integration and user interface. A case study in Karlskrona, Sweden, demonstrates its application, showing that DES enhances transparency, facilitates stakeholder communication, and supports iterative solution refinement. The results highlight the architecture’s potential to support sustainable urban waterborne transport and decision-making under uncertainty.

This paper proposes the MKSARMAX model for modeling and forecasting time series that can only take on values within a specified range, such as in the interval (0,1). The model is especially good for modeling double-bounded hydro-environmental time series since it accommodates bounded support and asymmetric distribution, making it advantageous compared to the traditional Gaussian-based time series model. The MKSARMAX models the conditional median of a modified Kumaraswamy distributed variable observed over time, by a dynamic structure considering stochastic seasonality and including autoregressive and moving average terms, exogenous regressors, and a link function. The conditional maximum likelihood method is employed to estimate the model parameters. Hypothesis tests and confidence intervals for the parameters of the proposed model are derived using the asymptotic theory of the conditional maximum likelihood estimators. Quantile residuals are defined for diagnostic analysis, and goodness-of-fit tests are subsequently implemented. Synthetic hydro-environmental time series are generated in a Monte Carlo simulation study to assess the finite sample performance of the inferences. Moreover, MKSARMAX outperforms beta SARMA, SARMAX, Holt-Winters, and KARMA models in most accuracy measures analyzed when applied to useful water volume datasets, presenting for the first-step forecast at least 98% lower MAE, RMSE, and MAPE values than competitors in the Caconde UV dataset, and 54% lower MAE, RMSE, and MAPE values than competitors in the Guarapiranga UV dataset. These findings suggest that the MKSARMAX model holds strong potential for water resource management. Its flexibility and accuracy in the early forecasting steps make it particularly valuable for predicting flood and drought periods.

Exploration of THz frequencies has enabled new applications in the areas, where high-resolution short-range remote sensing is of great interest. At the high frequency ranges like THz or sub-THz, synthetic aperture radar (SAR) image formation algorithms are highly sensitive to platform deviations, which motivates the development of new algorithms that are capable to handle this problem.In this paper, we introduce a modified linear interpolation algorithm with the phase-control procedure for the three-dimensional (3D) global backprojection (GBP) algorithm. The procedure assigns the actual time needed for the signal to be transmitted to the given point in the defined image plane and collected by the receiver into the phase of the complex SAR data. The algorithm has been verified with the real monostatic and bistatic SAR data acquired in the 0.325-0.5 THz frequency band.

The use of THz frequencies has enabled the opportunity to perform synthetic-aperture radar (SAR) imaging at the sub-mm level. It is of great interest for applications where high-resolution remote sensing in short range is required. However, with the increase of the operating frequencies from microwave to THz, the SAR image formation algorithms work with a larger amount of data and become more sensitive to phase errors that can be caused by insufficient signal sampling rate or physical factors that cause platform deviations. This motivates the use of fast image formation capable to handle phase errors. In this paper, we present the experimental results on the performance of the local backprojection (LBP) algorithm for processing THz SAR signals. The LBP algorithm has been tested with the real data in the frequency range 0.325-0.5 THz. The results demonstrate the efficiency of the LBP algorithm for the SAR scene reconstruction and highlight the necessity of the use of two times higher signal upsampling to achieve reconstruction accuracy similar to the global backprojection algorithm. 

The use of THz frequencies for radar remote sensing has provided a new set of applications, where short-range sensing for high-resolution imaging can be used. At the same time, radar imaging of physically large objects for understanding their radar signatures, which is based on long-range remote sensing, can be a cumbersome process and require significant financial resources. In this paper, we propose to explore the synthetic-aperture-radar (SAR) concept at sub-THz frequencies to scale a real-life scenario of long-range imaging of foreign objects down to short-range sensing in the indoor environment. The idea is experimentally studied on four downscaled marine vessels, where the SAR imaging system is based on a D-band FMCW radar that operates at frequencies 126-182 GHz, and the global backprojection algorithm with modified linear interpolator is used for the SAR scene reconstruction. The experimental results demonstrate the feasibility of the proposed idea. 

Among the growing elderly population, the number of people with neurocognitive disease increases, highlighting the need for early diagnosis. Mini-Mental State Examination (MMSE) is one of the tools used to diagnose neurocognitive disease. The existence of a relationship between degraded oral health and decreased MMSE scores is known. Using machine Learning (ML) techniques, the present study aimed to study the potential of using oral health and demographic examination data to indicate the level of MMSE score. Data from a study evaluating oral health over time and data from an ongoing study evaluating the general health in an elderly population were used as inputs to ML models Random Forest (RF), Support Vektor Machine (SVM), and Catboost (CB) for the binary indication of MMSE score 30 or MMSE score 26 or lower was used to find the best classification performance to distinguish between MMSE low and healthy control (HC) groups. The classifiers were trained using the nested cross-validation (nCV) method to mitigate the risk of overfiting. CB and RF achieved the highest classification accuracy of 80%. However, the CB classifier outperformed other classifiers with 76.4 average accuracies over all the nCV combinations. The oral health parameters and demographics used as input to the ML classifiers carry enough information to distinguish between MMSE low and HC groups. This study suggests that oral health examination might provide information that can be used with the help of Machine Learning (ML) to indicate lowered MMSE scores.

Text mining techniques, particularly those leveraging machine learning for natural language processing, have gained significant attention for qualitative data analysis in software testing. However, their complexity and lack of transparency can pose challenges, especially in safety-critical domains where simpler, interpretable solutions are often preferred unless accuracy is heavily compromised. This study investigates the trade-offs between complexity, effort, accuracy, and utility in text mining and clustering techniques, focusing on their application for detecting functional dependencies among manual integration test cases in safety-critical systems. Using empirical data from an industrial testing project at ALSTOM Sweden, we evaluate various string distance methods, NCD compressors, and machine learning approaches. The results highlight the impact of preprocessing techniques, such as tokenization, and intrinsic factors, such as text length, on algorithm performance. Findings demonstrate how text mining and clustering can be optimized for safety-critical contexts, offering actionable insights for researchers and practitioners aiming to balance simplicity and effectiveness in their testing workflows. 

Background: Recently, the demand for care has risen, while in contrast, healthcare resources remain limited. These resources include health expenditure, the number of physicians, nurses, and hospital beds. Many studies have revealed that healthcare resources are one of the most critical factors contributing to a population’s health status. The healthcare system plays a key role in transforming these resources into health outcomes, which are widely used as indicators to measure population health and the performance of healthcare systems. Previous work has primarily investigated the relationship between health expenditure or the number of doctors and population health. However, the association between healthcare resources as a whole has yet to be widely examined.

Methods: This study utilized multilevel regression analysis to explore the association between healthcare resources, healthcare systems, and population health outcomes across 25 European countries. The healthcare systems in these countries are primarily categorized into two types: Beveridge-type and Bismarck-type. In addition to regression analysis, descriptive statistics were used to analyze the allocation patterns of healthcare resources. Welch’s t-test was employed to compare the performance metrics of the Beveridge-type and Bismarck-type healthcare systems, providing a statistical basis for understanding differences in their effectiveness.

Results: The regression analysis revealed positive correlations between health expenditure per capita, the number of physicians, and nurses, and life expectancy at birth, while the number of hospital beds showed a negative correlation. Conversely, infant mortality was negatively correlated with health expenditure per capita and the number of physicians and nurses, and positively correlated with the number of hospital beds. The models did not find statistical significance in the effects of healthcare system type (Beveridge-type or Bismarck-type) on life expectancy at birth or infant mortality rates. Additionally, Welch’s t-test indicated that the Beveridge-type healthcare system generally showed better performance outcomes compared to the Bismarck-type system.

Conclusions: The findings indicate that higher allocations of certain healthcare resources, such as hospital beds, are associated with poorer health outcomes, which suggests potential inefficiencies in resource utilization. Observations also show that countries using the same healthcare systems tend to have similar patterns of resource allocation, which may influence the performance of these systems. Policymakers should consider these associations when planning resource allocation and when selecting or modifying healthcare system models in their countries. 

Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction model (N-XGB), a hybrid model that combines neural networks (NN) for feature extraction with XGBoost for classification. The NN component extracts high-level features, while XGBoost uses gradient-boosted decision trees for accurate predictions, combining the strengths of deep learning and boosting techniques for improved accuracy. The N-XGB model achieves an accuracy of 94.8% and an average F1 score of 0.95 on a real-world dataset that includes wildfires, floods and earthquakes, significantly outperforming baseline models such as random forest, Support vector machine and logistic regression 85% accuracy. The balanced F1 scores for wildfires 0.96, floods 0.93, and earthquakes 0.96 demonstrate the model's robustness in multi-class classification. The Synthetic Minority Oversampling Technique (SMOTE) balances datasets and improves model efficiency and capability. The proposed N-XGB model provides a reliable and accurate solution for predicting disasters and contributes to improving preparedness, resource allocation and risk management strategies. 

The microchannel heat sink is a commonly used structure in mechanical cooling systems for microelectronics. Based on bionics, a simplified heat sink with a spider-web design is proposed in this paper. Under the condition of bottom heat flux q = 100 W/cm2 and Reynolds number (Re) = 442-884, the influence of three parameters (main channel width, branch width and rib width) on the performance of a spider web microchannel heat sink (SW-MCHS) under different Re conditions was numerically analyzed by computational fluid dynamics. The results showed that the main channel had the greatest influence on the Nusselt number (Nu) and the Euler number (Eu); With the increase of main channel width, Nu increased by 46.97%, and Eu decreased by 31.74%. Rib width had the smallest influence on Nu and Eu; AWith the increase of rib width, Nu decreased by 7.18%, and Eu decreased by 12.00%. Based on the research results, the correlations for predicting Nu and Eu of the SW-MCHS were fitted; the Radj2 values for the two correlations were 0.9523 and 0.9246, respectively. These fitting correlations could be used to predict Nu and Eu for the SW-MCHS. The present study has contributed to advancing the applications of microchannel heat sinks and enhancing the cooling efficiency of mechanical microelectronics cooling systems.

Increasing sustainability expectations requires support for the design of systems that are reactive in minimizing potential negative impact and proactive in guiding engineering decision-making toward more value-robust long-term decisions. This article identifies a gap in the methodological support for the design of circular systems, building on the hypothesis that computer-based simulation models will drive the development of more value-robust systems designed to behave positively in a changeable operational environment during the whole lifecycle. The article presents a framework for value-robust circular systems design, complementing the current approaches for circular design aimed at increasing decision-makers' awareness about the complexity of circular systems to be designed. The framework is theoretically described and demonstrated through its applications in four case studies in the field of construction machinery investigating new circular solutions for the future of mining, quarrying and road construction. The framework supports the development of more resilient and sustainable systems, strengthening the feedback loop between exploring new technologies, proposing innovative concepts and evaluating system performance.

Context: Systematic literature studies, such as secondary studies, are crucial to aggregate evidence. An essential part of these studies is the selection phase of relevant studies. This, however, is time-consuming, resource-intensive, and error-prone as it highly depends on manual labor and domain expertise. The increasing popularity of Large Language Models (LLMs) raises the question to what extent these manual study selection tasks could be supported in an automated manner.

Objectives: In this manuscript, we report on our effort to explore and evaluate the use of state-of-the-art LLMs to automate the selection phase in systematic literature studies.

Method: We evaluated LLMs for the selection phase using two published systematic literature studies in software engineering as ground truth. Three prompts were designed and applied across five LLMs to the studies’ titles and abstracts based on their inclusion and exclusion criteria. Additionally, we analyzed combining two LLMs to replicate a practical selection phase. We analyzed recall and precision and reflected upon the accuracy of the LLMs, and whether the ground truth studies were conducted by early career scholars or by more advanced ones.

Results: Our results show a high average recall of up to 98% combined with a precision of 27% in a single LLM approach and an average recall of 99% with a precision of 27% in a two-model approach replicating a two-reviewer procedure. Further the Llama 2 models showed the highest average recall 98% across all prompt templates and datasets while GPT4-turbo had the highest average precision 72%.

Conclusions: Our results demonstrate how LLMs could support a selection phase in the future. We recommend a two LLM-approach to archive a higher recall. However, we also critically reflect upon how further studies are required using other models and prompts on more datasets to strengthen the confidence in our presented approach. © 2025 The Authors

Digital Forensics (DF) in distributed environments faces significant challenges, ranging from scalability, complexity, and reliance on traditional DF processes. The problem being addressed in this paper, is the lack of effective automated DF analysis across distributed ecosystems. Inspired by the success of peer-to-peer (P2P) architectures, and as a step toward overcoming the limitations of traditional client-server models,  a Distributed Adaptive Push-button Forensic (DAPF) System that leverages a decentralized approach is suggested. The DAPF system automates attack data collection and analysis across multiple nodes in an adaptive approach  to streamline DF investigations. Preliminary experiments have demonstrated a 30% reduction in analysis time compared to traditional methods. This work highlights the potential of automation, adaptability, and decentralized architectures in modern DF a step towards distributed digital forensics.

Effective evacuation planning is a complex challenge that aims to save lives, reduce travel time, and ensure the provision of essential care to vulnerable individuals. Evacuation simulation models are vital tools for evaluating different scenarios, identifying potential bottlenecks, and optimising strategies for large-scale evacuations.This report contains original and unpublished work that examines how these tools and their components can strengthen disaster preparedness and support decision-makers in managing the complexities of emergency response. It kicks off by outlining the key performance indicators (KPIs) relevant to large-scale evacuation, to then review current simulation approaches and their foundational building blocks. These include functions, algorithms, and models used to simulate and analyse various stages of evacuation: from pre-evacuation processes and individual decision making after a warning is issued, to traffic assignment, road network dynamics, vehicle behaviours,and shelter capacity management.The research work primarily focuses on macroscopic and mesoscopic simulation models, highlighting the strengths and limitations of the different paradigms and frameworks discussed in the literature. The report also features a curated selection of commercially available software, providing a timeline of their development, a comparison of key features, and insights into their real-world application in evacuation planning. This overview is further enriched with a series of case studies illustrating how these tools have been employed in disaster and crisis scenarios around the world.This report is published by Blekinge Institute of Technology (BTH) and is funded by the Swedish Civil Contingencies Agency (Myndigheten för samhällsskydd och beredskap, MSB) as part of the project Digital Decision Support for Large-Scale Evacuation (DISTURB). The DISTURB project is financed through the 2:4 Crisis Preparedness Fund (Anslag 2:4 Krisberedskap), which aims tosupport initiatives that strengthen society’s ability to manage crises and their consequences, and to develop and maintain the capacity for heightened civil defence preparedness.

Introduction: This project aimed to develop a Person-Centred Co-ordinated Care (P3C) pathway for low back pain (LBP).

Description: A national working group was formed consisting of representatives from all regional healthcare organisations in Sweden and included all relevant healthcare professions, academia, and patient organisations. A mixed method iterative design and consensus approach was applied in the development of the P3C pathway.

Discussion: As a foundation, patient interviews along with a review of literature were conducted investigating the evidence base for healthcare interventions, earlier regional care programs/pathways and guidelines in Sweden as well as patient experiences and challenges with healthcare for LBP. Updated evidence-based clinical recommendations, tools supporting the practical use of the national P3C pathway and national healthcare data registry-based quality outcome indicators were then developed. Thereafter, an open consultation period provided review and feedback for final revisions and consensus.

Conclusions: Essential factors for integrating best praxis according to scientific evidence and patient and healthcare professional perspectives were identified to establish a Swedish national P3C pathway for LBP. This provides a novel and innovative example of feasible methodology applicable in the international context. Future research will evaluate potential improvements in healthcare quality outcomes and effectiveness of dissemination and implementation strategies.

Accurate monitoring of draw-in behaviour during sheet metal forming is crucial for understanding material flow, optimizing process parameters, and validating finite element (FE) simulations. This study presents an integrated approach combining high-resolution optical measurement, laser displacement sensors, and numerical simulations to analyse draw-in variations during the first forming operation of an automotive front door inner panel. A dedicated optical system was employed to capture sequential images of the blank edge, which were calibrated and processed using computer vision techniques to extract precise draw-in values at predefined locations. The results demonstrate that optical monitoring provides reliable insights related to the sheet metal forming process, highlighting the influence of real-world process disturbances. Furthermore, the study explores the feasibility of integrating measured draw-in data into an adaptive control framework, applying artificial intelligence techniques to refine process stability. By utilizing experimental data alongside numerical predictions, this methodology enhances process understanding and enables data-driven decision-making in industrial sheet metal forming. The findings contribute to the development of intelligent forming control strategies, bridging the gap between modelling and real-world manufacturing conditions to improve product quality and production efficiency.

Reducing the CO2 footprint has become a key objective in the manufacturing sector, with the automotive industry being no exception. A significant portion of a car body consists of stamped components, making the reduction of CO2 emissions in stamping lines a critical focus. One major contributor to emissions is the high material usage, partially derived from the scrap generation. Additionally, production ramp-ups in critical components, such as side door-inners and wheel housings, often lead to increased defect rates, further exacerbating waste. This work investigates the influence of stamping tool temperature increases during production and its consideration in the early stages of process design. Using a Volvo side door inner as an industrial benchmark, various numerical solutions were explored using AutoForm software. The technical note presents the advantages, limitations, and challenges of these approaches, while highlighting the potential of numerical tools to drive CO2 footprint reduction in stamping processes from an early design stage.

One of the main challenges in Synthetic Aperture Radar (SAR) change detection involves using SAR images from different flight passes. Depending on the flight pass, objects have different specular reflections since the radar cross-sections of these objects can be totally different between passes. Then, it is common knowledge that the flight passes must be close to identical for conventional SAR change detection. Wavelength-resolution SAR refers to a SAR system with a spatial resolution approximately equal to the wavelength. This high relative resolution helps to stabilize the ground clutter in the SAR images. Consequently, the restricted requirement about identical flight passes for SAR change detection can be relaxed, and SAR change detection becomes possible with nonidentical passes. This paper shows that robust principal component analysis (RPCA) is efficient for change detection even using wavelength-resolution SAR images acquired with very different flight passes. It presents several SAR change detection experimental results using flight pass differences up to 95°. For slightly different passes, e.g., 5°, our method reached a false alarm rate (FAR) of approximately one false alarm per square kilometer for a probability of detection (PD) above 90%. In a particular setting, it achieves a PD of 97.5% for a FAR of 0.917 false alarms per square kilometer, even using SAR images acquired with nonidentical passes.