Improving And Enhancing Scenario Planning: Futures Thinking

This book presents a contemporary view of the approaches and theories informing global scenario planning and foresight science. The collection of chapters details new and broader views of futures thinking, scenario methodologies, innovative developments, and practical applications that reflect the evolution of the field into the 21st century. The collection of insights is not exhaustive, but rather serves as a bridge from last century’s foundations into this century’s innovations. Our global team of authors span multiple generations and demographics, representing industry leaders, award winning scientists, consultants, and emerging researchers. The chapters are divided across six sections that reflect the path of scenario development, from foundation to validation. This book is best viewed as the latest addition to the robust library of existing volumes in scenario planning and foresight science. The purpose of the book is to provide insights with guidance for practitioners and support for academics

An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata

A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vertical correlation can be exploited to launch privacy attacks. In this paper, a novel privacy preserving model lc, ls-ANGEL is proposed. To validate the new model, two privacy attacks are presented, namely, a Vertical correlation attack (Vc0) and a Vulnerable sensitive attribute attack (Vsa) on 1:M datasets, which breach the privacy of individuals. Furthermore, the proposed model is examined through High-Level Petri Nets (HLPNs). Our experiments on three real-world datasets;“INFORMS”,“YOUTUBE”, and “IMDb” demonstrate that the proposed model outperforms the state-of-the-art models. Our practices and lessons learned in this work can direct future concrete steps towards Multiple Sensitive Attributes, where we can expand the proposed model to dynamic dataset

Energy-Capital Substitution, Technological Innovation, and Monetary Policy

China has been actively implementing a green development strategy focused on peak carbon and carbon neutrality. The challenge is to avoid the economic fluctuations caused by energy price increases, promote effective substitution of capital for energy, stimulate innovation in energy utilization technology, and implement appropriate monetary policies to resist the negative impact of external supply shocks on the macro economy. This study constructs a new energy utilization technology progress equation within the NK-DSGE framework to reveal the mechanism of energy price-induced technological progress and clarify the substitution path between energy and capital. The study finds that: (1) Rising energy prices drive broad supply-side cost hikes, notably harming capital efficiency and diminishing capital's substitutability for energy. This worsens factor allocation efficiency, potentially inducing an overall demand decrease, thus causing sustained adverse effects on the economy and society (2) China's current energy technology partly reduces economic fluctuations from energy price shocks. Rising energy prices can drive firms to enhance their energy technology, easing the adverse effects of energy price fluctuations. The study notes that the extent of energy technology mitigation of price shocks relies on energy-capital substitution efficiency in production. Advanced energy technology fosters better energy-capital substitutability, curbing the duration and severity of economic stagflation triggered by energy cost hikes. (3) a monetary policy that focuses solely on the core inflation target is more effective than one that focuses on "temporary" price fluctuations represented by energy prices, implying that central banks need a clear policy target system when formulating monetary policies

Multi-scale integration with semantic embedding and adaptive excitation transformer for underwater optical image enhancement

As an important technology in the domains of ocean exploration and underwater robotics, underwater optical image enhancement has drawn significant attention. However, underwater images suffer from severe degradation due to wavelength-related optical attenuation, refraction, and scattering. The need to acquire high-quality underwater optical images poses challenges to current techniques. A dual-branch neural network based on multi-scale integration with semantic embedding and adaptive excitation transformer for underwater optical image enhancement is proposed in this paper. First, a semantic embedding network serving as the semantic branch is introduced to extract low- and high-level semantic features effectively to generate images with clearer edges and texture details. Concurrently, an enhancement branch containing adaptive excitation transformer is constructed to enhance local details while concentrating on global information. Since different feature channels correspond to different patterns of the input image, an adaptive excitation mechanism is deployed to achieve adaptive estimations of channel weights, highlighting the more representative channels while penalizing less important channels to enrich texture and eliminate blurring and color deviations. Additionally, a multi-scale dynamic integration module is designed. It establishes a correlation between the semantic and enhancement branches and adaptively selects prominent features to avoid over-enhancement and improve clarity and color deviation. Both the qualitative and quantitative results evaluated on public underwater optical image datasets show that our method outperforms the state-of-the-art methods in terms of subjective perception and evaluation metrics, indicating outstanding learning and generalization capabilities. Furthermore, excellent performance highlights the substantial benefits it contributes to downstream visual-related engineering tasks

patter: Particle algorithms for animal tracking in R and Julia

State‐space models are a powerful modelling framework in movement ecology that represents individual movements and the processes connecting movements to observations. However, fitting state‐space models to animal‐tracking data can be difficult and computationally expensive. Here, we introduce patter, a package that provides particle filtering and smoothing algorithms that fit Bayesian state‐space models to tracking data, with a focus on data from aquatic animals in receiver arrays. patter is written in R, with a performant Julia backend. Package functionality supports data simulation, preparation, filtering, smoothing and mapping. In two examples, we demonstrate how to implement patter to reconstruct the movements of a tagged animal in an acoustic telemetry system from acoustic detections and ancillary observations. With perfect information, the particle filter reconstructs the true (unobserved) movement path (Example One). More generally, particle algorithms represent an individual's possible location probabilistically as a weighted series of samples (‘particles’). In our illustration, we resolve an individual's (unobserved) location every 2 min during 1 month and use particles to visualise movements, map space use and quantify residency (Example Two). patter facilitates robust, flexible and efficient analyses of animal‐tracking data. The methods are widely applicable and enable refined analyses of space use, home ranges and residency

Prolonged Hospital Stay in Hypertensive Patients: Retrospective Analysis of Risk Factors and Interactions

Background/Objectives: Arterial hypertension (HT) is a leading modifiable risk factor for cardiovascular diseases, often contributing to prolonged lengths of hospital stay (LOHS), which place significant strain on healthcare systems. This study aimed to analyze the factors associated with prolonged lengths of hospital stay in patients with HT, focusing on key biochemical and clinical predictors. Methods: This retrospective study included 356 adult patients hospitalized in the Cardiology Department of the University Hospital in Wroclaw, Poland, between January 2017 and June 2021. Data collected included demographic characteristics, body mass index (BMI), comorbidities, and laboratory parameters. Logistic regression models were used to identify predictors of prolonged LOHS, defined as four or more days, and to evaluate interactions between variables. Results: Lower levels of low-density lipoprotein cholesterol (LDL-c) and elevated concentrations of high-sensitivity C-reactive protein (hsCRP) were identified as significant predictors of prolonged LOHS, with each 1 mg/dL decrease in LDL-c increasing the odds of prolonged LOHS by 1.21% (p < 0.001) and each 1 mg/L increase in hsCRP raising the odds by 3.80% (p = 0.004). An interaction between sex and heart failure (HF) was also observed. Female patients with HF had 3.995-fold higher odds of prolonged LOHS compared to females without HF (p < 0.001), while no significant difference was found among male patients with or without HF (p = 0.890). Conclusions: The predictors of prolonged LOHS in patients with HT include lower levels of LDL-c, elevated hsCRP, and the interaction between sex and heart failure (HF). Specifically, female patients with HF demonstrated significantly higher odds of prolonged LOHS compared to females without HF, while this relationship was not observed in male patient

Brain 3T magnetic resonance imaging in neonates: features and incidental findings from a research cohort enriched for preterm birth

Background and objectives The survival rate and patterns of brain injury after very preterm birth are evolving with changes in clinical practices. Additionally, incidental findings can present legal, ethical and practical considerations. Here, we report MRI features and incidental findings from a large, contemporary research cohort of very preterm infants and term controls.Methods 288 infants had 3T MRI at term-equivalent age: 187 infants born <32 weeks without major parenchymal lesions, and 101 term-born controls. T1-weighted, T2-weighted and susceptibility-weighted imaging were used to classify white and grey matter injury according to a structured system, and incidental findings described.Results Preterm infants: 34 (18%) had white matter injury and 4 (2%) had grey matter injury. 51 (27%) infants had evidence of intracranial haemorrhage and 34 (18%) had punctate white matter lesions (PWMLs). Incidental findings were detected in 12 (6%) preterm infants. Term infants: no term infants had white or grey matter injury. Incidental findings were detected in 35 (35%); these included intracranial haemorrhage in 22 (22%), periventricular pseudocysts in 5 (5%) and PWMLs in 4 (4%) infants. From the whole cohort, 10 (3%) infants required referral to specialist services.Conclusions One-fifth of very preterm infants without major parenchymal lesions have white or grey matter abnormalities at term-equivalent age. Incidental findings are seen in 6% of preterm and 35% of term infants. Overall, 3% of infants undergoing MRI for research require follow-up due to incidental findings. These data should help inform consent procedures for research and assist service planning for centres using 3T neonatal brain MRI for clinical purposes

Comparing talent development environments of girls and boys in handball and ice hockey in Norway

Currently, there is little research on successful talent development environments (TDEs) focusing on women and girls. In response, the main aim of the present study was to compare TDEs of age‐specific national teams for girls and boys in the Norwegian context (N = 216: 92 girls and 124 boys). Gender differences were investigated in the two different sports of handball and ice hockey, which in the Norwegian context represent more and less successful sports (handball and ice hockey, respectively). Before investigating gender differences in the two sports, a necessary first step was to investigate the psychometric properties of Norwegian version of the Talent Development Environment Questionnaire (TDEQ‐5). Results support the Norwegian TDEQ‐5 to be a reliable and valid measure within the Norwegian context. The successful sport of Norwegian handball showed no significant gender differences regarding TDE. The less successful and male dominated sport of Norwegian ice hockey showed girls to score lower on several TDEQ factors compared to boys. Results also showed ice hockey having lower TDEQ scores compared to handball. We argue that handball provide similarly functional TDEs for girls and boys, making gender equality a characteristic feature of a TDE that is successful both in terms of mass participation and international achievements

Experiences and Health Outcomes of Emerging Adults with Type 1 Diabetes: A Mixed Methods Study

Background Emerging adults with type 1 diabetes are at risk of poorer diabetes-related health outcomes than other age groups. Several factors affecting the health and experiences of the emerging adults are culture and healthcare specific.Objectives The aim of this study was to explore the experience of emerging adults living with type 1 diabetes in Lebanon, describe their diabetes self-care and diabetes-related health outcomes (HbA1c and diabetes distress), and identify the predictors of these outcomes.Methods A convergent mixed methods design was used with 90 participants aged 18-29 years. Sociodemographic, clinical data, and measures of diabetes distress, social support, and self-care were collected. Fifteen emerging adults participated in individual semi-structured interviews. Multiple linear regression was used to determine predictors of diabetes outcomes. Thematic analysis was used to analyze qualitative data. Data integration was used to present the mixed methods findings.Results The study sample had a mean HbA1c of 7.7% (SD = 1.36) and 81.1 % reported moderate to severe diabetes distress levels. The participants had good levels of diabetes self-care and high levels of social support. HbA1c was predicted by insulin treatment type, age at diagnosis, and diabetes self-care; while diabetes distress was predicted by diabetes knowledge, blood glucose monitoring approach, and diabetes self-care. “Living with type 1 diabetes during emerging adulthood: the complex balance of a chemical reaction” was the overarching theme of the qualitative data, with three underlying themes: “Breaking of bonds: changes and taking ownership of their diabetes”, “The reactants: factors affecting the diabetes experience”, and “Aiming for equilibrium”. The integrated mixed methods results revealed one divergence between the qualitative and quantitative findings related to the complexity of the effect of received social support.Discussion The suboptimal health of the emerging adults despite good self-care highlights the importance of addressing cultural and healthcare specific factors such as diabetes knowledge and public awareness, social support, and availability of technology to improve diabetes health. Findings of this study can guide future research, practice, and policy development