Background:The treatment for patients with colorectal cancer with metastases to the peritoneum is complex and may involve both surgery and chemotherapy. Circulating tumor cells (CTCs) have been poorly investigated in peritoneal metastatic colorectal cancer. The aim of the study is to examine the role of CTCs as a biomarker for monitoring disease progression, treatment response, and residual disease using CellMate - a new promising in vitro diagnostic platform technology.Materials and methods:The authors prospectively followed the clinical outcomes of 46 patients treated with cytoreductive surgery and hyperthermic intraperitoneal chemotherapy for colorectal cancer with peritoneal metastases and examined whether CTCs were present the week of surgery. The CTC measurements were made with the CellMate technology, which is a platform technology to detect CTCs based on the difference in biomechanical properties compared to blood resident cells. The study was registered online (ClinicalTrials.gov).Results:CTCs were detected in 17 (37%) patients. The presence of CTCs was associated with shorter recurrence-free survival and overall survival after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy. Both recurrence-free survival (HR 4.00, 95% CI: 1.15-13.9; P=0.029) and overall survival (HR 5.91; 95% CI: 1.18-29.7; P=0.03) were significantly worse if CTCs were detected after neoadjuvant treatment. In the subgroup of patients with CTCs detected, adjuvant therapy tended to improve the prognosis while in CTC negative patients it did not.Conclusions:Pending a prospective multicenter trial to validate these findings, CTCs may in the future be used as a dynamic personalized biomarker for prognostication, predicting response to therapy, and for monitoring disease progression in colorectal cancer with metastases to the peritoneum.

Den här rapporten beskriver framtagandet av två GIS-skikt som visar känsliga och värdefulla undervattensmiljöer i Stockholms skärgård för användning i digitala sjökort. Vi beskriver även framtagandet av rekommendationer till båtförare i dessa områden. 

Rapporten är framtagen inom projektet Ekosystembaserad havsförvaltning (EBHF) Stockholms skärgård som startades av Havs- och vatten-myndigheten 2021 och som drivs av Länsstyrelsen Stockholm. Projektet samlar aktörer verksamma vid kusten i Stockholms län samt myndigheter och forskare. Syftet är att kartlägga kustens ekosystem och den mänskliga påverkan, samt att genomföra åtgärder för att förbättra havsmiljön. En av projektets grupper arbetar bland annat med frågor om fritidsbåtar och deras påverkan på havsmiljön. Gruppen samlar representanter från båtlivet, miljöorganisationer, lokala organisationer, boende, markägare, förvaltare, tjänstemän från kommuner och länsstyrelse samt forskare från Stockholms universitet och Sveriges lantbruksuniversitet.

Metoden som presenteras här har tagits fram av rapportförfattarna, som är forskare vid Stockholms universitet, i dialog med deltagarna i arbetsgruppen för naturskydd och fritidsbåtar i EBHF Stockholms skärgård och med sjökortstillverkare och företag som tillhandahåller sjökortsdata.

The interaction between emergency vehicle drivers and surrounding road users is associated with risks. This study explored the application of geofencing to improve interactions between drivers and emergency vehicles to reduce the risk of collisions in high-risk scenarios. Two high-risk scenarios, an off-ramp collision, and an intersection scenario, were used in two driving simulator experiments with 64 participants in total. Half of the drivers received a geofence-based warning about the upcoming traffic situation. The results indicate that geofencing, when applied to provide warnings in specific locations, improves driver behavior. In the off-ramp experiment, all drivers who received a warning avoided the off-ramp and thereby avoided the collision site, whereas all other drivers took the off-ramp. In the intersection experiment, the warning led to earlier deceleration, allowing the emergency vehicle to pass safely and with minimal delay; whereas nearly half of those who did not get a warning failed to yield to the emergency vehicle. The drivers acted based on the warning they received, even when they had not yet seen the emergency vehicle. The findings suggest that geofencing can improve driver behavior by detecting emergency vehicles early and reliably, thereby improving traffic safety and minimizing delay for emergency vehicles on call. 

Life cycle assessment (LCA) studies of lithium-ion batteries (LIB) often show a large variation in results. Explanations to this diversity can be found in different modelling approaches and shifting quality of data, e.g. a lack of primary data and use of outdated data, but also a large variety in cell formats, chemistries, and designs analysed [1], [2]. Meanwhile, LCA of LIBs is becoming increasinglyimportant as a tool to guide technology selections towards increased sustainability. To bridge existingdata gaps, a multidisciplinary cooperation has been initiated aiming to develop a model platform for conducting LCA of LIBs in vehicle applications, covering both production and the use-phase. This model platform will comprise a cell design computation model, a cell performance model, a vehicle model, and a production LCA inventory model, which provide input the overall LCA.

In this contribution, the cell design computation model and cell performance model will be presented. The cell design computation model provides input for the production LCA inventory and cell performance models. For a given capacity, the model calculates required masses of constituent materials, electrode areas and thicknesses by assuming common cell design aspects and the volume for the selected cell format. Input data is based on publications and teardown analyses of commercial cells. The cell performance model utilises the physics-based Doyle-Fuller-Newman model implemented in COMSOL Multiphysics. From this, the cell capacity, potential, and impedance can be simulated for any cell design. Both these data models then supply data further to the production LCA inventory model and the vehicle model, in turn enabling the overall LCA.

 Differentiating subjects through the comparison of their recorded speech is a common endeavor in speaker characterization. When using an acoustic-based approach, this task typically involves scrutinizing specific acoustic parameters and assessing their discriminatory capacity. This experimental study aimed to evaluate the speaker discriminatory power of vowel formants—resonance peaks in the vocal tract—in two different speaking styles: Dialogue and Interview. Different testing procedures were applied, specifically metrics compatible with the likelihood ratio paradigm. Only high-quality recordings were analyzed in this study. The participants were 20 male Brazilian Portuguese (BP) speakers from the same dialectal area. Two speaker-discriminatory power estimates were examined through Multivariate Kernel Density analysis: Log cost-likelihood ratios (Cllr) and equal error rates (EER). As expected, the discriminatory performance was stronger for style-matched analyses than for mismatched-style analyses. In order of relevance, F3, F4, and F1 performed the best in style-matched comparisons, as suggested by lower Cllr and EER values. F2 performed the worst intra-style in both Dialogue and Interview. The discriminatory power of all individual formants (F1-F4) appeared to be affected in the mismatched condition, demonstrating that discriminatory power is sensitive to style-driven changes in speech production. The combination of higher formants ‘F3 + F4’ outperformed the combination of lower formants ‘F1 + F2’. However, in mismatched-style analyses, the magnitude of improvement in Cllr and EER scores increased as more formants were incorporated into the model. The best discriminatory performance was achieved when most formants were combined. Applying multivariate analysis not only reduced average Cllr and EER scores but also influenced the overall probability distribution, shifting the probability density distribution towards lower Cllr and EER values. In general, front and central vowels were found more speaker discriminatory than back vowels as far as the ‘F1 + F2’ relation was concerned.

The International Material Data System (IMDS) can be used as data source for life cycle assessments (LCAs) in the automotive industry. The level of data aggregation and degree of completeness affect precision of LCA results and required workload. This paper assesses this trade-off. Life cycle impact assessment scores for an engine, modelled as detailed as possible, were compared to results for seven simplified modelling options. The study concludes that: (1) employing IMDS data with lower resolution reduced the workload marginally; (2) cutting-off materials below 1 wt-% greatly decreased workload while maintaining reasonable precision; (3) decreasing the number of substances representing each material largely affected scores for most impact categories except a few, including the climate change category, while (4) excluding complementary data for manufacturing significantly impacted greenhouse gas emissions. Since modelling choices affect the impact categories differently, aligning choices with the purpose of the study and available workload is paramount.

Hyaluronan (HA) has gained significant attention in cancer research for its role in modulating chemoresistance. This review aims to elucidate the mechanisms by which HA contributes to chemoresistance, focusing on its interactions within the tumor microenvironment. HA is abundantly present in the extracellular matrix (ECM) and binds to cell-surface receptors such as CD44 and RHAMM. These interactions activate various signaling pathways, including PI3K/Akt, MAPK, and NF-kappa B, which are implicated in cell survival, proliferation, and drug resistance. HA also influences the physical properties of the tumor stroma, enhancing its density and reducing drug penetration. Additionally, HA-mediated signaling contributes to the epithelial-mesenchymal transition (EMT), a process associated with increased metastatic potential and resistance to apoptosis. Emerging therapeutic strategies aim to counteract HA-induced chemoresistance by targeting HA synthesis, degradation, metabolism, or its binding to CD44. This review underscores the complexity of HA's role in chemoresistance and highlights the potential for HA-targeted therapies to improve the efficacy of conventional chemotherapeutics.

The genetic diversity that underpins biodiversity is shaped by a combination of evolutionary forces, including mutation, migration, genetic drift, and natural selection. The advent of sequencing technologies has enabled genetic studies to be scaled up, both in terms of the number of individuals and the number and types of genetic markers considered. In particular it has shown that structural variation captures more genetic diversity than single nucleotide polymorphisms. Extensive population genomics studies across species ranges have also revealed much more permissive boundaries between species than previously thought. A comprehensive understanding of the origins of genetic diversity and the factors that shape it would therefore require large-scale studies spanning a range of biological levels and scales, from individual genomes to whole populations, and from point mutations to large structural variations. We have addressed these questions in the case of Norway spruce (Picea abies) and Siberian spruce (Picea obovata), two boreal forest tree species with continental ranges.

In Paper I, we used genome-wide SNPs to show that extensive gene flow exists between Norway spruce and Siberian spruce, and we used coalescent simulations to reveal the occurrence of repeated hybridization events between the two species across glacial cycles. These events had a profound impact on the evolutionary trajectories of both species, and a large hybrid zone now extends from northwestern Europe to the southern Urals. Paper II is an eco-evolutionary study in which we demonstrated the role of hybridization in expanding both the species' ecological niche breadth and their resilience to climate change. In Paper III, we investigated patterns of adaptation at two different geographical scales (a latitudinal cline across Sweden and a longitudinal cline across both species' ranges), focusing on the role of gene copy number variations (gCNVs). We showed that a significant proportion of genes have copy number variations and that they are distributed across the genome. These gCNVs are associated with responses to abiotic and biotic stresses, including drought tolerance, temperature regulation and immune responses. Genotype-environment association also revealed that gCNVs play an important role in adaptation along environmental gradients, probably because of the quantitative response they allow. Importantly, we did not detect these candidate genes with conventional methods using SNP data. This work on gCNVs was made possible by the development of a comprehensive framework for detecting CNVs from single nucleotide polymorphism (SNP) data, presented in Paper IV. Paper V further extends the method under a composite likelihood ratio framework to be used with whole genome resequencing data.

With the advent of electromobility, life cycle assessment studies need to keep up with growing number of cell formats and chemistries being adopted for various vehicle applications. This often hindered by lack of data. A model platform is presented, starting with a cell design computation model which is used for calculating the mass of cell components and other design parameters. It also includes a cell performance model, which will link to a battery pack and vehicle model, both used for estimate losses caused by the cell during vehicle operation. Furthermore, the platform comprises a model generating inventory data for life cycle assessment of lithium-ion battery cell production. Together, these parts feed information to life cycle assessment calculations covering both production and use of lithium-ion battery cells. The aim is to supporttechnology development and provide an understanding of how various design changes in cells link toenvironmental impacts. This conference paper explains model parts and provides exemplary results. 

This study investigates drivers’ eye gaze behavior in response to Emergency Vehicle Approaching (EVA) warnings. EVA warnings, delivered through in-car alerts, provide advance notice of approaching EVs, enabling drivers to move over in time. Previous research indicates that EVA warnings influence driver behavior positively, promoting safer interactions. This study expands further by exploring the role of system introduction to make drivers benefit from EVA warnings. A simulator experiment with 73 participants was conducted. Before driving, half of the participants were introduced to the EVA system. The participants were driving on a highway and were overtaken by EVs twice during a 20-minute drive. During the drive, half of participants received EVA warnings. Gaze distribution was analyzed in three areas of interest (Forward, Mirrors, Dashboards). Analysis of driving simulator data did not reveal any differences in driving behaviors. However, the analysis of drivers’ gaze distribution suggests that EVA warnings contribute to increased mirror usage, indicating early scanning for approaching EVs. Furthermore, drivers who were introduced to the EVA system before driving but never received an EVA warning in the simulator looked through the front windshield less than drivers who were introduced and received an EVA warning. This study contributes to understanding the driver gaze behavior when receiving an in-car warning for emergency vehicles and supports previous findings regarding EVA warnings positive impact of driver behavior.

Life cycle assessment (LCA) literature evaluating environmental burdens from lithium-ion battery (LIB) production facilities lacks an understanding of how environmental burdens have changed over time due to a transition to large-scale production. The purpose of this study is hence to examine the effect of upscaling LIB production using unique life cycle inventory data representative of large-scale production. A sub-goal of the study is to examine how changes in background datasets affect environmental impacts.

We remodel an often-cited study on small-scale battery production by Ellingsen et al. (2014), representative of operations in 2010, and couple it to updated Ecoinvent background data. Additionally, we use new inventory data to model LIB cell production in a large-scale facility representative of the latest technology in LIB production. The cell manufactured in the small-scale facility is an NMC-1:1:1 (nickel-manganese-cobalt) pouch cell, whereas in the large-scale facility, the cell produced in an NMC-8:1:1 cylindrical cell. We model production in varying carbon intensity scenarios using recycled and exclusively primary materials as input options. We assess environmental pollution–related impacts using ReCiPe midpoint indicators and resource use impacts using the surplus ore method (ReCiPe) and the crustal scarcity indicator.

The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has convinced many industries to shift towards semi-automated assembly lines with human-robot collaboration (HRC). In the HRC environment, robot agility can support human skill upon efficiently balancing tasks among the stations and operators. On the other hand, the robot energy consumption in today's energy crisis area demands that tasks be performed with as little energy utilization as possible by robots. In this context, the cycle time (CT) and total energy cost (TEC) of robots are among two conflicting objectives. Thus, this study balances HRC lines where a trade-off between CT and TEC of robots is sought. A mixed-integer linear programming model is proposed to formulate the problem. In addition, a multi-objective optimization approach based on epsilon-constraint is developed to address a case study from the automotive industry and a set of generated test problems. The computational results show that promising Pareto solutions in terms of CT and TEC can be obtained using the proposed approach.

The interaction between emergency vehicle drivers and surrounding road users is associated with risks. This study explored the application of geofencing to improve interactions between drivers and emergency vehicles to reduce the risk of collisions in high-risk scenarios. Two high-risk scenarios, an off-ramp collision, and an intersection scenario, were used in two driving simulator experiments with 64 participants in total. Half of the drivers received a geofence-based warning about the upcoming traffic situation. The results indicate that geofencing, when applied to provide warnings in specific locations, improves driver behavior. In the off-ramp experiment, all drivers who received a warning avoided the off-ramp and thereby avoided the collision site, whereas all other drivers took the off-ramp. In the intersection experiment, the warning led to earlier deceleration, allowing the emergency vehicle to pass safely and with minimal delay; whereas nearly half of those who did not get a warning failed to yield to the emergency vehicle. The drivers acted based on the warning they received, even when they had not yet seen the emergency vehicle. The findings suggest that geofencing can improve driver behavior by detecting emergency vehicles early and reliably, thereby improving traffic safety and minimizing delay for emergency vehicles on call.

Brain tumors pose a significant global health challenge, driving ongoing research advancements in early detection methods. Artificial intelligence (AI) and deep learning (DL) techniques have shown great potential in this field, enabling the creation of highly accurate models for brain tumor identification from medical images. However, centralized approaches to these methods often raise critical concerns regarding patient data privacy and security. This paper presents a novel federated learning (FL) framework for brain tumor identification that effectively addresses these privacy concerns. FL enables collaborative model training across multiple institutions without the need for raw data sharing. Each participating institution trains the model locally on their Magnetic Resonance Imaging (MRI) datasets and only transmits model updates to a central server for secure aggregation. This iterative process results in a robust global model trained on a distributed dataset while preserving patient data confidentiality. The proposed FL model is evaluated using a dataset of 3,000 MRI images. Experimental results demonstrate the effectiveness of our approach, achieving a high accuracy rate of 96.88% for brain tumor identification. These findings suggest that FL provides a viable solution for privacy-preserving brain tumor identification, maintaining comparable performance to centralized models while ensuring the security of patient data.

This study investigates early childhood education and care (ECEC) teachers’ reported practices and views regarding children’s invented writing. A total of 222 teachers from Finland, Norway, and Sweden responded to a questionnaire on their practices regarding invented writing activities and rated statements on a Likert scale. The data were analyzed quantitatively with descriptive statistics, and open answers were analyzed qualitatively using content analysis. The results showed that few teachers arranged daily writing activities and that writing was especially uncommon with children aged three years and younger. A dominant view expressed was that children needed to be of a certain age for writing to be relevant and that the children were simply too young. The writing that did take place focused on letters and children’s names. Although most teachers reported that the alphabet was visibly displayed in their ECEC units, few teachers had included writing materials in play settings. However, some teachers reported physical and social learning environments that engaged children in invented writing. We believe that the latter teachers’ accounts indicate a didactic approach in Nordic ECEC that could serve as a starting point for future investigations regarding the didactics of invented writing in ECEC.

Biodegradable nanomaterials can significantly improve the safety profile of nanomedicine. Germanium nanoparticles (Ge NPs) with a safe biodegradation pathway are developed as efficient photothermal converters for biomedical applications. Ge NPs synthesized by femtosecond-laser ablation in liquids rapidly dissolve in physiological-like environment through the oxidation mechanism. The biodegradation of Ge nanoparticles is preserved in tumor cells in vitro and in normal tissues in mice with a half-life as short as 3.5 days. Biocompatibility of Ge NPs is confirmed in vivo by hematological, biochemical, and histological analyses. Strong optical absorption of Ge in the near-infrared spectral range enables photothermal treatment of engrafted tumors in vivo, following intravenous injection of Ge NPs. The photothermal therapy results in a 3.9-fold reduction of the EMT6/P adenocarcinoma tumor growth with significant prolongation of the mice survival. Excellent mass-extinction of Ge NPs (7.9 L g⁻¹ cm⁻¹ at 808 nm) enables photoacoustic imaging of bones and tumors, following intravenous and intratumoral administrations of the nanomaterial. As such, strongly absorbing near-infrared-light biodegradable Ge nanomaterial holds promise for advanced theranostics.

Background:Three-dimensional (3D) printing produces objects by adding layers of material rather than mechanically reducing material. This production technology has several advantages and has been used in various medical fields to, for instance, improve the planning of complicated operations, customize medical devices, and enhance medical education. However, few existing studies focus on the adoption and the aspects that could influence or hinder the adoption of 3D printing.ObjectiveTo describe the state of 3D printing in Sweden, explore the perceived effects of using 3D printing, and identify barriers to its adoption.MethodsA qualitative study with respondents from seven life science regions (i.e., healthcare regions with university hospitals) in Sweden. Semi-structured interviews were employed, involving 19 interviews, including one group interview. The respondents were key informants in terms of 3D printing adoption. Data collection occurred between April and May 2022 and then between February and May 2023. Thematic analysis was applied to identify patterns and themes.ResultsAll seven regions in Sweden used 3D printing, but none had an official adoption strategy. The most common applications were surgical planning and guides in clinical areas such as dentistry, orthopedics, and oral and maxillofacial surgery. Perceived effects of 3D printing included improved surgery, innovation, resource efficiency, and educational benefits. Barriers to adoption were categorized into organization, environment, and technology. Organizational barriers, such as high costs and lack of central decisions, were most prominent. Environmental barriers included a complex regulatory framework, uncertainty, and difficulty in interpreting regulations. Technological barriers were less frequent.ConclusionsThe study highlights the widespread use of 3D printing in Swedish healthcare, primarily in surgical planning. Perceived benefits included improved surgical precision, innovation, resource efficiency, and educational enhancements. Barriers, especially organizational and regulatory challenges, play a significant role in hindering widespread adoption. Policymakers need comprehensive guidance on 3D printing adoption, considering the expensive nature of technology investments. Future studies could explore adoption in specific clinical fields and investigate adoption in non-life science regions within and outside Sweden.

Rechargeable batteries are used in a number of applications of high societal importance, including various types of electronics and electric vehicles. Many of those batteries currently in use contain rare and/or critical chemical elements and materials. In this study, we identify next-generation battery chemistries based on a survey sent out to organizations within the Batteries Sweden (BASE) competence centre. The identified chemistries are then assessed regarding their resource requirements, applying a screening resource assessment method developed within the study. The method considers the crustal rarity and criticality of the materials contained within the battery cell, from the perspective of the European Union. The results from the screening assessment show that two types of multivalent batteries (one specific calcium-based battery cell and one specific aluminium-based cell) contain the lowest number of rare and critical materials of the batteries assessed, while a certain type of lithium-ion battery cell (nickel-manganese-cobalt, NMC) contains the highest number of rare and critical materials. The developed screening method can be used by BASE members and other relevant actors to identify battery chemistries with promising resource performance for further, more detailed resource assessments, such as life cycle assessment and material flow analysis.  

The overall objective of the project was to contribute to the development of remediation methods for PFAS-contaminated materials. The performance of different destruction methods such as sonolysis, fenton reagent addition, electrochemical oxidation and ultraviolet radiation was evaluated to optimize operational variables and potential for scalability. Combinations of destructive remediation methods as well as non-destructive techniques such as soil washing/wet sieving and adsorption were evaluated for remediation of PFAS contaminated soil and water from Umeå airport and a landfill site (DÅVA DAC). Electrochemical oxidation performed better than the other degradation methods studied with degradation rates of up to 99% regarding PFOA.

Based on the promising results for electrochemical oxidation, it was chosen for the demonstration trials with a scaled-up flow cell reactor. Soil washing was found to effectively remove PFAS, and a treatment train of soil washing followed by foam fractionation and electrochemical oxidation resulted in 99% removal of PFOS. Adsorption followed by electrochemical oxidation removed 84.5% of PFOS from the groundwater samples and 76% PFOS from a landfill leachate. The project indicates a potential for these scalable remediation methods for PFAS-contaminated materials but also points to the need for further work on process optimization and scalability. 

In order to reduce the global dependency on fossil fuels by adopting renewable energy technologies and advancing electromobility, batteries are a key technology. Lithium-ion batteries (LIBs) are currently the dominant rechargeable battery technology, mainly due to their high energy density. However, most LIBs contain a number of geochemically scarce metals, e.g. cobalt, lithium and nickel. The production of LIBs is furthermore associated with considerable environmental impacts. Battery researchers and companies therefore try to develop the next generation batteries (NGBs) with the same or even higher energy densities than LIBs, while requiring less of scarce metals and causing lower environmental impacts. One promising NGB technology is the lithium-sulfur (Li-S) battery, with a potential to significantly improve energy density as compared to current state-of-the-art LIBs. Although Li-S batteries still face a number of scientific and technical challenges, they have a significant advantage over LIBs from a resource point of view: the cells do not require any scarce metals besides lithium. Using prospective life cycle assessment, we will assess the life-cycle environmental impacts of Li-S batteries and compare them to those of LIBs, both modeled at large-scale production. In order to investigate the effect of using less scarce metals on resource impacts, the mineral resource impact category will be given extra attention. We will therefore include a range of mineral resource impact assessment methods, e.g. the abiotic depletion indicator, the surplus ore indicator, and the recently developed crustal scarcity indicator, which takes an explicit long-term perspective on elemental resources in the Earth’s crust. The overall aim is thus to compare the prospective life-cycle impacts of this particular NGB to those of LIBs, with a focus on mineral resources.   

Platform thinking involves creating an ecosystem that enables different participants to interact and co-create value. This mindset shift moves away from the traditional linear value chain model toward a more collaborative, networked approach. In the manufacturing sector, platform thinking has given rise to new business models like manufacturing as a service (MaaS) and cloud manufacturing (CMfg). In this paper, building upon existing literature, a system dynamics (SD) model is developed to analyze the acceptance of platform thinking as an innovative solution in manufacturing. In contrast to previous studies, the model considers the dynamics of customer behavior in addition to the roles of the platform provider (government) and service providers (manufacturers). The model incorporates four decision variables: commission rates, user membership fees, and subsidies for both cloud platform users and service providers, which can be adjusted to inform policymakers. Studying the effect of individual decision variables over a 30-year time frame using the SD model reveals that increasing commission rates and user membership fees negatively affect the platform size (number of manufacturers joining and staying on the platform), yet can result in increased long-term platform revenue. On the other hand, providing subsidies for cloud platform users and service providers has a positive impact on the platform size but leads to increased costs for the platform provider. A non-dominated sorting genetic algorithm (NSGA-II) is employed to optimize the system dynamics model and identify optimal policy settings that balance the interests of all stakeholders. This approach enables policymakers to make informed decisions that support the growth of CMfg and MaaS, fostering innovation and competitiveness in the manufacturing sector.

Objective: Previous research points to a role of the brain in the regulation of glucose and pathogenesis of type 2 diabetes (T2D) via modulation of counter-regulatory hormone secretion and activity in the autonomic nervous system (ANS). The aim of this study was to investigate glucose-dependent responses of catecholamines and ANS activity in individuals with T2D, prediabetes (PD), and normoglycemia (NG). Design: Cross-sectional.

Methods: Individuals with T2D (n = 19, 7 men, HbA1c 49 mmol/mol), PD (n = 18, 8 men), and NG (n = 17, 3 men) underwent 1 stepwise hyperinsulinemic-euglycemic-hypoglycemic and 1 hyperglycemic clamp with repeated measurements of catecholamines, symptoms, heart rate variability (HRV), and hemodynamics.

Results: The hypoglycemic response of adrenaline was augmented in T2D and PD vs NG (both P < .05), and there was a strong association with insulin resistance (P < .05 for M-value). In relation to achieved glucose levels in both clamps, noradrenaline exhibited a steeper rise during hypoglycemia in T2D vs NG and PD (both P < .05). There were trends toward more marked autonomic hypoglycemic symptoms in T2D vs PD and NG. By contrast, insulin resistance was associated with attenuated responses of heart rate and HRV indices P-LF and P-HF at the target glucose plateau of 2.7 mmol/L (P < .05), independent of BMI and HbA1c.

Conclusion: Alterations in glucose-dependent responses of counter-regulatory hormones and the ANS appear before, and probably contribute to, the onset of T2D. Together with other reported alterations in neuroendocrine pathways, the findings suggest that a maladaptation of the brain's responses to glucose fluctuations is important in T2D progression.

This study aims to incorporate building and demolition waste, including lime and crushed granite, as partial alternatives for cement and fine aggregates, respectively, and to devise a plan to reduce their environmental effect resulting from their extensive prevalence in substantial amounts. The use of lime in paste, mortar, and concrete has become a common practice to regulate the environment, save resources, and improve performance in various settings. The first stage of this study investigated the effects of replacing different proportions (0%, 15%, 25%, 35%, and 50%) of lime powder with cement on the physical and mechanical properties of mortar specimens over 7, 28, and 90 days. The next phase of the research examined the impacts of substituting varying quantities (ranging from 10% to 100%) of granite powder in 15 different mixes, while keeping a consistent water-to-binder ratio of 0.45. The last part of the study consisted of an examination of data from previous research on cement mortar and lime-modified cement mortar. This included testing on flowability, standard consistency, setting time, flexural strength, and compressive strength. The acquired data underwent a statistical analysis, which resulted in the development of equations that may predict the mechanical characteristics of changed cement mortar mixes. These equations also highlight the impact of certain physical qualities on compressive and flexural strength. 

An assessment method for sustainability was developed by the authors in a previous article. Many social sustainability assessment methods rely on assessors’ subjective judgments, which can be problematic. This study aims to examine the level of consensus different assessors can achieve using various assessment methods and to compare their results with an assessment made by one of the authors, to reduce subjectivity. A selective sample of engineering students from Karlstad University were surveyed to test and compare three as-assessment methods against the initial assessment. The three methods are: Woxnerud’s (the authors’) method, Jan Gehl’s twelve quality criteria, and a structured survey. Seven student groups conducted the first assessment, followed by 12 individual students who performed the second and third assessments. The objectives were to determine whether multiple assessors could reach a consensus using each method, identify which method yielded the most consensus and was most effectively implemented, and measure each method’s consonance rate in relation to Woxnerud’s initial assessment. The first method achieved a 75.0% assessor consensus and 98.4% consonance. The second method achieved a 67.0% assessor consensus and 75.0% consonance. The third method achieved a 50% assessor consensus and 91.0% consonance. This limited study suggests that a subjective method, such as an assessment method for social sustainability, can yield somewhat similar results, and in addition, Woxnerud’s method is more objectively applicable than the two other methods tested in this article. 

Enjoyment is a crucial yet complex indicator of positive user experience in Human-Robot Interaction (HRI). While manual enjoyment annotation is feasible, developing reliable automatic detection methods remains a challenge. This paper investigates a multimodal approach to automatic enjoyment annotation for HRI conversations, leveraging large language models (LLMs), visual, audio, and temporal cues. Our findings demonstrate that both text-only and multimodal LLMs with carefully designed prompts can achieve performance comparable to human annotators in detecting user enjoyment. Furthermore, results reveal a stronger alignment between LLM-based annotations and user self-reports of enjoyment compared to human annotators. While multimodal supervised learning techniques did not improve all of our performance metrics, they could successfully replicate human annotators and highlighted the importance of visual and audio cues in detecting subtle shifts in enjoyment. This research demonstrates the potential of LLMs for real-time enjoyment detection, paving the way for adaptive companion robots that can dynamically enhance user experiences.

Flow field measurements around cross-country skiers (xc skiers) are lacking in the literature to date. The aim was therefore to investigate the possibility of using particle tracking velocimetry for visualization and measurement of the flow field around xc skiers roller skiing on a treadmill in a wind tunnel. The airflow was seeded with neutrally buoyant helium-filled soap bubbles as tracer particles, following the flow without affecting it. As illumination, two different approaches were tested: first, a laser in the cameras’ line of sight (sagittal plane), then an LED unit directed vertically in a narrow slice, clearly limiting the depth of the measurement volume in the cameras’ line of sight. The flow field was studied at various speeds (3-7 m/s) around a single skier as well as around two skiers in line with the streaming airflow. It was found that the experimental approach has the potential to provide detailed insights, both qualitatively and quantitatively, into the flow field dynamics. The main challenges regarding setup, illumination, seeding, and cameras were identified, and possible improvements to streamline the experimental methodology were discussed. 

In indirect detection, sample components lacking detectable properties are detected by adding a detectable component to the eluent, a so-called probe that interacts with the analytes to be detected. This study focuses on modeling indirect detection in two principally different cases. In case (1), the analyte component has the same charge as the probe component, so the probe acts as a co-ion of the analyte. In case (2), the analyte component has the opposite charge to the probe, so the probe acts as a counter-ion of the analyte. In the co-ion case (1), the analytes are alkyl sulfonates, and a competitive bi-Langmuir isotherm model was used. In the counter-ion case (2), the analytes are amines, and a modified bi-Langmuir isotherm model, incorporating ion-pairing on the stationary phase surface, was derived and applied for simulating the elution profiles. The chromatographic system comprised an XBridge Phenyl column as the stationary phase and an acetonitrile/phosphate buffer mixture with varying concentrations of sodium 2-naphthalenesulfonate as the eluent. In both cases, the detectable probe component was sodium 2-naphthalenesulfonate. The applied isotherm models successfully predicted system peaks with high agreement in both model cases, with calculated relative errors in retention times typically below 4.72 % and often below 1 %. The models were employed to predict the sensitivity of analytical methods, demonstrating excellent agreement between experimental and calculated sensitivities. These findings confirm the validity of the new adsorption isotherm model under these experimental conditions. 

Engineering polymers stand out as the predominant dielectric materials in triboelectric nanogenerators (TENGs), primarily owing to their robust triboelectric effect and widespread availability. However, growing environmental concerns surrounding these polymers have prompted a notable shift towards exploring alternative eco-friendly materials, with cellulose materials emerging as compelling contenders over the past few years. Cellulose, derived from various sources and presented in diverse forms and structures, has found utility as triboelectric materials. In contrast to many engineering polymers known for their chemical stability, cellulose materials exhibit heightened chemical activities. This characteristic provides a unique opportunity to delve into fundamental questions in TENGs by manipulating the physical and chemical properties of cellulose materials. This concise critical review aims to thoroughly examine the applications of cellulose materials while shedding light on the opportunities presented by these versatile materials.

Den här rapporten är del av ett nordiskt projekt vars övergripande syfte har varit att undersöka hur regionalpolitik på bästa sätt kan bidra till att tillgodose civil beredskap och robusthet i nordiska landsbygder under perioder av kris. 

I det nya risklandskapet är ett robust samhälle, med kapacitet att stå emot och hantera både miljökatastrofer och mänskligt orsakade kriser, högst eftersträvansvärt. Regioner och kommuner kan bidra till robusthet och civil beredskap genom att upprätthålla samarbetsförmåga och utveckla innovativa lösningar för en hållbar omställning, tillika säkra tillgången till mat, råmaterial, energi och service för sina invånare, både i fredstid och i perioder av kris.

Inom ramen för studien definieras civil beredskap genom följande aspekter: förebyggande och trygghetsskapande åtgärder i samhället, räddningstjänst och krisberedskap, matsäkerhet inklusive livsmedelsförsörjning, jordbruk och matproduktion, energiförsörjning, försörjning av råmaterial (främst mineraler och metaller), samt tillgång till (offentlig) service.

Robotized laser beam welding is one important process in manufacturing, offering efficient welding while minimizing the heat input. Nonetheless, this method is sensitive to various deviations, including fixture problems, heat-induced distortions, and inaccuracies in tool handling. Such deviations can lead to significant defects like lack of fusion, particularly when welding square butt joints without gaps. Detecting these defects through visual inspection or non-destructive methods is challenging. To address this, real-time monitoring and automatic intervention are necessary. One effective sensor for monitoring laser beam welding is the photodiode, which captures optical emissions from the process. Research has demonstrated correlations between these emissions and process stability. Photodiodes are costeffective and easily integrated into welding tools, making them ideal for industrial applications. However, the challenge lies in analyzing the output signals and defining thresholds for identifying deviations from normal conditions. Thus, there's a need for an automated method to set threshold values based on measured data. Machine learning offers a solution, particularly through supervised, unsupervised, or semi-supervised methods. Supervised machine learning requires labeled data, involving time-consuming experiments with nominal and deviating cases, making it less feasible for industrial setups. This paper suggests using unsupervised learning for anomaly detection, relying solely on data from nominal welding cases for model training. In this approach, a model is trained using photodiode data from a single nominal weld case and subsequently tested on data collected during experiments involving laser beam offsets during welding. The results demonstrate the promise of this method for monitoring closed square butt-joint laser beam welding, even with limited training data from nominal cases. 

Rapporten innehåller analyser och resultatredovisning från det nationella provet i matematik för årskurs 3. Syftet med de nationella proven i årskurs 3 är att stödja bedömningen av elevernas kunskaper i relation till kriterierna för bedömning av kunskaper. De nationella proven kan också bidra till att stärka skolornas kvalitetsarbete genom analyser av provresultaten i relation till uppnådda bedömningskriterier på skolnivå, huvudmannanivå och på nationell nivå.

I denna rapport redovisas resultat och analyser baserade på PRIM-gruppens insamling av elevernas resultat från det nationella provet i årskurs 3 samt resultat från lärarnas svar på lärarenkäten. Underlaget är cirka 560 besvarade enkäter och nära 830 slumpvis utvalda elevlösningar och resultat på samtliga delprov.

I rapporten beskrivs ett urval av elevers skriftligt kommunicerade problemlösningsstrategier samt en analys av vanligt förekommande felsvar i tre uppgifter. I lärarenkäten framkommer att både lärare och elever i hög grad är positivt inställda till provet. Nio av tio lärare anser att provet som helhet är ett stöd för bedömningen och att provets kravgränser är lämpliga.

International Journal of Exercise Science 17(3): 308-326, 2024.

Objective: Anorexia Nervosa (AN) has one of the highest mortality rates of all mental health disorders, low recovery rate and is associated with widespread endocrine dysfunction. Resistance training (RT) has been consistently shown to provide beneficial effects on health outcomes that are often negatively affected by AN, however participation in exercise is controversial for individuals with AN. The objective of this study was to assess the effects of maximal RT as an add-on to standard of care in patients with AN.

Methods: Originally, a controlled clinical trial was planned but due to COVID-19 pandemic, the study was prematurely ended and reported as a case series design. Three female inpatients with AN (Age 18-29 years, body mass index (BMI) 14.5-16.3 kg/m2, illness duration 1-7 years) underwent a supervised 6-week RT intervention as an add-on to standard of care. Primary outcome was muscular strength, as measured by a 1-repetition maximum. Secondary outcomes included BMI, eating disorder psychopathology and maladaptive exercise tendencies.

Results: No adverse events were reported. All three participants improved lower body muscle strength, ranging from 32% to 134% in the leg press. Changes of 4% to 134% in the bench press and-3% to 38% in the pulldown were also observed.

Conclusions: RT improved muscular strength in the participants. RT as part of standard of care may also provide additional benefits for individuals with AN, although further research is required to determine which subtype of patients would benefit from the addition of RT to their treatment protocol.

Since the state-of-the-art Li-ion batteries are close to reaching their theoretical limit in energy density, it becomes crucial to develop next-generation batteries that enable better safety, higher energy density, and longer lifetime. One such next-generation technology is solid-state batteries, employing solid-state electrolytes. Both polymer and inorganic electrolytes are well-explored in this context. While polymers are flexible and easily processable, their ionic conductivities are generally insufficient. Inorganic ceramics can be good ionic conductors, but display interfacial issues. Therefore, combining polymeric and ceramic material in composites polymer electrolytes (CPEs) can – in principle – be beneficial to merge the advantages of both categories. However, it is still unclear how to best construct such systems, and how the ions are actually transported in them. 

This thesis explores ionic transport in CPEs, both with ion-conducting (“active”) and non-ion-conducting (“passive”) ceramic fillers. The focus is on the amorphous polymer material poly(trimethylene carbonate) (PTMC), the active ceramic filler Li₇La₃Zr₂O₁₂ (LLZO), and the passive ceramic fillers LiAlO₂ (LAO) and NaAlO₂ (NAO). The ionic transport mechanism in PTMC:LLZO CPEs is determined to be dependent on two main factors: particle loading and surface chemistry. An increase in ionic conductivity up to 30 wt% of Li₇La₃Zr₂O₁₂ is seen due to formation of additional transport pathways along the polymer-ceramic interfaces, while higher loadings affect the ionic conductivity negatively. While this can partly be explained by particle agglomeration, the presence of Li₂CO₃ on the Li₇La₃Zr₂O₁₂ surface also contributes to retard the ionic movement along the interfaces. Therefore, boric acid treatment is explored as a strategy to enable a Li₂CO₃-free surface of Li₇La₃Zr₂O₁₂ particles, which renders improved ionic transport and battery performance. Boron-treated Li₇La₃Zr₂O₁₂ shows formation of LiBO₂, which yields a negative zeta-potential, indicative of interactions between the ceramic particles and Li⁺ ions. That the surface chemistry – rather than the bulk – of the ceramic filler ultimately controls the overall transport, opens the door towards employment of passive fillers. It is shown that LiAlO₂  particles can increase the ionic conductivity by one order of magnitude and the Li⁺ transference number to almost 1, effectively rendering the LiAlO₂-based CPE a single-ion conductor. These enhanced ionic transport properties can be explained by the ability of LiAlO₂  particles to promote better ion-ion separation through the attraction of negatively charged TFSI anions to the surface. This renders considerably improved battery performance, enabling cycling in Li||NMC cells. Similar effects are also seen for the analogous Na-ion battery system. 

Thereby, considering that the bulk conductivity of active fillers does not contribute to the overall ionic conduction in CPEs, and that passive fillers such as LiAlO₂  can greatly enhance the ionic transport because of its surface chemistry enabling greater ion-ion separation and favorable transport pathways, this thesis provides guidelines for future design of solid-state conductors for Li- and Na-batteries. 

Introduction

The LIB production has increased due to capacity upscaling in existing plants and new giga-scale plants becoming operational. However, environmental implications of this LIB production upscaling are not well understood. Additionally, there is lack of research on how background data in life cycle assessment (LCA) studies affects results. 

Method

• Cradle-to-gate life cycle assessment used to compare production in a small-scale (MWh/year) and large-scale (GWh/year) LIB production facilities.
• Small-scale factory modeled using different versions of Ecoinvent databases to analyze the effect of changing data availability and quality on overall results.
• Two different indicators (ReCiPe and Crustal Scarcity Indicator) used to analyze the resource use impacts.

Results

1. The climate change impacts of the large-scale facility reduce by 45% and 22% in the reference and the low-carbon intensity scenarios respectively, compared to the small-scale facility.
2. Changes in background databases are technological and data quality indicators and can affects the overall results of the study. 
3. From a long-term resource scarcity perspective, copper has the highest impacts followed by nickel and cobalt.

Deep learning-based models have revolutionized medical diagnostics by using Big Data to enhance disease diagnosis and clinical decision-making. However, their significant computational demands and opaque decision making processes, often characterized as ’black-box’ systems, pose major challenges in time-critical and resource constrained healthcare settings. To address these issues, this study explores the application of randomized machine learning models, specifically Extreme Learning Machines (ELMs) and Random Vector Functional Link (RVFL) networks, in medical diagnostics. These models introduce stochasticity into their training processes, reducing computational complexity and training times while maintaining accuracy. Furthermore, we integrate Explainable AI techniques namely Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) to explain the decision-making rationale of ELMs and RVFL. Performance evaluations on genitourinary cancers and coronary artery disease datasets demonstrate that RVFL outperforms traditional deep learning models, achieving superioraccuracyof88.29%withacomputationaloverhead of 6.22 seconds for genitourinary cancers, and an accuracy of 81.64% with a computational time of 0.0308 seconds for coronary artery disease. This research highlights the potential of randomized models in enhancing efficiency and transparency in medical diagnosis, thereby accelerating better treatment outcomes and advocating for more accessible and interpretable AI solutions in healthcare. 

This project addresses the significant impact of ammonia (NH₃), reactive nitrogen (Nr), and methane (CH₄) emissions from the agriculture sector on air pollution and climate change. By analyzing over 300 mitigation measures across the Nordic countries and beyond, it identifies integrated strategies that reduce the effects on both air pollution and climate change simultaneously. These measures can support the Nordic countries to comply with national and international commitments. The project highlights 17 integrated measures—primarily in manure and fertilizer management—capable of reducing both NH₃ and CH₄ emissions. Recommendations include policy enhancements, financial incentives, and improved coordination among agriculture, environment, and climate sectors to achieve sustainable agricultural practices and effective emissions reductions.

Fifth Generation (5G) systems have been commercially available worldwide for at least a couple of years, with mid-band Non-Standalone (NSA) being the deployment mode preferred by Mobile Network Operators (MNOs). Empirical analyses have provided so far key insights on 5G NSA performance from different perspectives, but most of these works consider short time periods to drive conclusions. In this paper, we investigate the evolution of 5G NSA considering deployment, performance, and services, including positioning. We perform a large-scale measurement campaign in two phases (2021 and 2023), covering six MNOs in two European countries, Italy and Sweden. Our results show significant differences in network deployment and performance, with increasing network density and frequencies but, at times, decreasing downlink throughput performance. For the latter, we identify worse radio coverage and connectivity issues as root causes. By using a standardized methodology, we also evaluate the performance of new services such as real-time gaming and augmented/virtual reality, and reveal that stable 5G connectivity is key to meet their requirements. Similarly, we highlight the negative effects of roaming on performance. Finally, we evaluate 5G fingerprinting positioning systems and show that a higher accuracy is achievable in denser 5G deployments.

As the environmental impact of food consumption gains increasing attention and the hospitality sector faces pressure to become sustainable, identifying mechanisms to promote pro-environmental food choices in out-of-home settings becomes crucial. This study investigates the impact of message endorsement on consumers' sustainable food choices in an à la carte restaurant. The study involves workshops with restaurant staff, joint intervention design, and a 6-week-long field experiment to test the effect of an environmental appeal message endorsed by an expert on consumers’ food choices. Data on menu item sales, nonparticipant observation and interviews are collected to evaluate the intervention's effectiveness. The study contributes new knowledge on behavioural factors driving sustainable food choices in out-of-home settings. It also offers practical insights for designing interventions that reduce the hospitality sector's environmental footprint without harming customer experience or profitability. Methodological implications concern new insights on field experiments conducted in collaboration with restaurant managers and staff.  

This paper examines the development of the Artificial Intelligence (AI) meta-debate in Sweden before and after the release of ChatGPT. From the perspective of agenda-setting theory, we propose that it is an elite outside of party politics that is leading the debate – i.e. that the politicians are relatively silent when it comes to this rapid development. We also suggest that the debate has become more substantive and risk-oriented in recent years. To investigate this claim, we draw on an original dataset of elite-level documents from the early 2010s to the present, using op-eds published in a number of leading Swedish newspapers. By conducting a qualitative content analysis of these materials, our preliminary findings lend support to the expectation that an academic, rather than a political elite is steering the debate. 

Retailers are increasingly leveraging emerging digital technologies to enhance and deliver memorable and timely customer experiences. However, more research is needed to understand how these technologies are being implemented. Therefore, we conducted an fsQCA analysis of 32 retail cases of early adopters of beacon technology. Our findings reveal three main modes of implementation of emerging digital technologies in the retail customer experience: automation, augmentation, and customization. These modes can be interpreted as enablers that enhance the value of utilitarian and/or hedonic experiences, rather than as drivers of innovation per se. Our study adds a new perspective to the debate about the role of digital technologies in driving innovation. Moreover, the three modes identified provide managers with practical guidelines for the implementation of emerging digital technologies in retail environments. 

A couple of applications of Bäcklund transformations in the study of nonlinear evolution equations is here given. Specifically, we are concerned about third order nonlinear evolution equations. Our attention is focussed on one side, on proving a new invariance admitted by a third order nonlinear evolution equation and, on the other one, on the construction of solutions. Indeed, via Bäcklund transformations, a Bäcklund chart, connecting Abelian as well as non Abelian equations can be constructed. The importance of such a net of links is twofold since it indicates invariances as well as allows to construct solutions admitted by the nonlinear evolution equations it relates.The present study refers to third order nonlinear evolution equations of KdV type. On the basis of the Abelian wide Bäcklund chart which connects various different third order nonlinear evolution equations an invariance admitted by the Korteweg-de Vries interacting soliton (int.sol.KdV) equation is obtained and a related new explicit solution is constructed. Then, the corresponding non-Abelian Bäcklund chart, showshow to construct matrix solutions of the mKdV equations: some recently obtained solutions are reconsidered. 

In Hjältevad, the former Televerket impregnated telephone poles with CCA salts (copper, chromium and arsenic) during the period 1949-1985. These activities led to the contamination of surface soil layers in the area as well as soil layers below the groundwater table. The groundwater in Hjältevad is anaerobic and arsenic is present in its mobile, reduced form, arsenite (As(III)). 

The present project investigated the possibility of immobilising As(III) using zero-valent iron (ZVI) sorbents, both at the nano- (nZVI) and micro-scale (mZVI), in both ‘pure’ form (nZVI or mZVI) and with a coating of iron sulfide (S-nZVI or S-mZVI). Batch and column experiments were performed under field relevant (anaerobic) conditions. The experiments showed that all sorbents had a good arsenic retention capacity (>90% reduction in solubility), but that the ‘pure’ sorbents (nZVI and mZVI) were slightly more effective than the sulfidated ones (S-nZVI and S-mZVI). The fixation of arsenic appears to be by binding to the corrosion products formed when ZVI reacts with water, i.e. various iron(III) (hydr)oxides. 

Pilot scale experiments with nZVI and mZVI showed that the major challenge in Hjältevad is to effectively disperse ZVI particles in the aquifer. Despite the addition of organic polymers, according to standard methodology, the radius of influence was shorter than expected, which probably explained why no effect on either key parameters (pH and redox potential) or arsenic concentration could be observed in groundwater wells installed in the two test plots.

This study uses a genetic algorithm(GA) to investigate the practicality of optimizing the geometry and dimensions of a floating platform, which houses pitching wave energy converters (WEC). Using frequency- domain analysis, sensitivity tests for the search start point, choice of optimized variable, number of iterations, simulation time, and contents of the search space are made. Results show that the required number of iterations to convergence increases with an increased number of optimized variables. Furthermore, for the studied platform geometry, no single global optimum exists. Instead, various combinations of characteristic features can lead to comparable performances of the integrated wave absorber. Finally, it is observed that when the solution space is controlled and made to contain a subset of potential solutions known to improve the system performance, computation time, absorption efficiency and range are observed to improve. Additionally, the GA optimum tends towards platform geometries for which the wave absorber's resonance response corresponds to the dominating wave climate frequencies. A key contribution of this study is the controlled manipulation of the solution space to contain a subset of potential solutions that enhance system performance. This controlled approach leads to improvements in computation time, absorption efficiency, and range of the system.

The family Turridae, once considered to encompass the entire diversity of Conoidea, excluding only the cone shells (family Conidae) and the auger shells (family Terebridae), has undergone significant redefinition based on both morphological and molecular data. Prior to this study, it included 15 Recent genera, comprising 209 species. However, a recent comprehensive analysis of an extensive dataset featuring 3,159 cytochrome c oxidase subunit I sequences has unveiled numerous species complexes, new species and previously undiscovered deep lineages. The newly proposed classification of Turridae based on an exon-capture phylogeny recognizes 24 clades, each worthy of genus rank, though only 13 of them had available names. As a result, 11 new genera have been described, namely Thielesyrinx n. gen., Shutogemmula n. gen., Mcleanigemmula n. gen., Pseudogemmula n. gen., Deceptigemmula n. gen., Oliveragemmula n. gen., Powelligemmula n. gen., Kilburnigemmula n. gen., Alisigemmula n. gen., Taylorigemmula n. gen. and Anisogemmula n. gen. With the exception of Thielesyrinx n. gen., species from all of these were previously classified within Gemmula, or Lophiotoma. Notably, some new genera, such as Deceptigemmula n. gen., Kilburnigemmula n. gen., Shutogemmula n. gen. and Taylorigemmula n. gen., cannot be morphologically differentiated, as similar species are found in other genera. Therefore, their distinction relies solely on molecular data. The name Eugemmula is re-instated from the synonymy of Gemmula. Purpuraturris is synonimized with Annulaturris, while Iotyrris with Xenuroturris. In total, we recognize 312 species/primary species hypotheses (PSHs) within the family. Molecular data were obtained for 212 species (among them 15 species complexes comprising in total 45 PSHs), mostly for the first time. A characteristic feature common to the entire family is the position of the relatively narrow anal sinus on the whorl periphery or immediately above it. Radula was examined in representatives of all genera. Although in several cases the radular morphology can aid genus differentiation (e.g. distinguishing Turris from Annulaturris), it is mostly a highly homoplastic structure, with very similar radular types occurring in distantly related genera.

The idea of Artificial intelligence (AI) has a long history in both research and fiction and has been applied in educational settings since the 1970s. However, the topic of AI underwent a huge increase of interest with the release of ChatGPT in late 2022, and more people were talking about generative AI (GenAI or GAI). According to some estimates, the number of publications on generative AI increased with 2269.49% between 2022 and 2023, and the increase was even higher when related to computing education. The aim of this study is to investigate the potential strengths, weaknesses, opportunities, and threats of generative AI in computing education, as highlighted by research published after the release of ChatGPT. The study applied a scoping literature review approach with a three-step process for identifying and including a total of 129 relevant research papers, published in 2023 and 2024, through the Web of Science and Scopus databases. Included papers were then analyzed with a theoretical thematic analysis, supported by the SWOT analysis framework, to identify themes of strengths, weaknesses, opportunities, and threats with generative AI for computing education. A total of 19 themes were identified through the analysis. Findings of the study have both theoretical and practical implications for computing education specifically, and higher education in general. Findings highlights several challenges posed by generative AI, such as potential biases, overreliance, and loss of skills; but also several possibilities, such as increasing motivation, educational transformation, and supporting teaching and learning. The study expands the traditional SWOT analysis, by providing a risk management strategy for handling the strengths, weaknesses, opportunities, and threats of generative AI.

Objective

Autoethnography combines personal experiences with cultural analysis, emerging as a response to the limitations of traditional ethnography. This review aimed to explore, describe, and delineate the utilization of autoethnography by nurses published in peer-reviewed journals.

Methods

A scoping review was conducted according to the Arksey and O’Malley framework. On October 12, 2023, autoethnographic studies in nursing were identified through searches of CINAHL, PubMed, PsycINFO, and Scopus. Peer-reviewed articles published in English language were retrieved. We applied no date restriction. Data were extracted on nursing, epiphany, results, style of writing, implications for nursing, and ethical considerations.

Results

Twenty-six articles met the inclusion criteria. Mental health nursing, covered by nine articles, elucidated experiences of stigma, ethical dilemmas, and professional identity. Nursing education, represented by seven articles, highlighted identity struggles, systemic biases, and evolving pedagogies. Palliative care, addressed by three articles, provided insights into communication challenges and emotional complexities in end-of-life care. The remaining articles explored rehabilitation, cultural competence, and chronic pain management. A conceptual framework integrating ontological, epistemological, ethical, and practical dimensions was developed, emphasizing the interplay between personal and professional roles.

Conclusions

This review underscores autoethnography’s value in uncovering the cultural and ethical dimensions of nursing. This framework advocates for a reflective, culturally attuned approach to healthcare, fostering transformative changes in nursing. Further research should explore underrepresented nursing specialties to harness autoethnography’s potential fully.