Determinants and effects of green bond issuance : Environmental awareness, ecological budget, biodiversity, oil and lithium

This study explores the determinants of green bond issuance and its environmental impacts. The empirical analysis employs a panel dataset from 29 OECD countries for the period 2014–2020 and two main models. The first model identifies determinants of green bond issuance, revealing that higher environmental awareness, GDP per capita, oil prices, a higher degree of urbanization, and lower lithium prices are associated with a higher volume of issued green bonds. The second model employs several indicators of environmental performance as dependent variables. The main independent variables are the issued green bond volume, environmental awareness, GDP per capita, environmental policy stringency, renewable energy capacity, and the share of protected areas. The results show that green bond volume, stringent environmental policy, and higher environmental awareness are positively related to the ecological budget and biodiversity while reducing the ecological footprint. Channels for this impact are positive relationships between green bond funding and renewable energy capacity and the share of protected areas. Governments should therefore not only promote the issuance of green bonds, which are essential to raise the financial resources needed to finance environmentally friendly projects, but also offer tax and investment incentives, provide technical assistance, and simplify procedures for project implementation. In addition, resources should be devoted to raising environmental awareness among the population

Entwicklung und Validierung eines Instruments zur Erfassung selbstregulierten Lernens in Lernprodukten

Die Analyse von studentischen Lernprodukten ermöglicht eine verhaltensnahe Diagnostik von kognitiven Lernstrategien und liefert eine Grundlage für die Evaluation von Lernaktivitäten. In der vorliegenden Studie wird untersucht, ob Organisations- und Elaborationsstrategien anhand von Lernprodukten reliabel erfasst und am Lernerfolg validiert werden können. Mit einem neu entwickelten Codierschema wurden 229 Vorlesungszusammenfassungen von 24 Lehramtsstudierenden anhand verschiedener hoch-inferenter Kategorien analysiert, die eine effiziente Auswertung ermöglichen. Für tiefere Einblicke wurden darüber hinaus 94 Vorlesungszusammenfassungen von 10 Studierenden mit besonders hohem bzw. niedrigem Lernerfolg mit niedrig-inferenten Kategorien ausgewertet. Die Ergebnisse zeigen für die hoch-inferenten Kategorien eine substanzielle und für die niedrig-inferenten Kategorien eine sehr gute Interrater-Reliabilität. Es konnten signifikante Korrelationen zwischen der Nutzung von Lernstrategien im Lernprodukt und dem Lernerfolg (Prüfungsergebnis) der Studierenden nachgewiesen werden. Studierende mit hohem Lernerfolg nutzten Lernstrategien fast aller Kategorien häufiger als solche mit niedrigem Lernerfolg. Grenzen und Potentiale des Ansatzes für die Diagnostik und Evaluation selbstregulierter Lernprozesse werden diskutiert.The analysis of students’ learning products enables a behavioral diagnosis of cognitive learning strategies and provides new information for the evaluation of learning activities. The current study aims to analyze whether organization and elaboration strategies can be measured reliably in learning products and can be validated by learning success. With a newly developed coding scheme 229 lecture summaries of 24 student teachers were examined by different high-inferent categories, which enable an efficient evaluation of the learning products. For deeper insights, 94 lecture summaries of 10 students with either high or low learning success were analytically rated by means of low-inferent categories. Overall results show an interrater reliability that is substantial for high-inferent categories and very good for low-inferent categories. Significant correlations between the usage of learning strategies for the learning products and learning success (examgrades) could be found. Students with high learning success used learning strategies of nearly all categories more frequently as those with low learning success. Limitations and potentials of the approach for diagnosing and evaluating self-regulated learning processes are discussed

Power Balance and Relationship Quality : an Overstated Link

Power balance, that is, equal levels of potential influence between relationship partners, has been linked to relationship happiness. This study examined whether power balance is indeed positively related to relationship quality (RQ) for both couple members using dyadic response surface analysis (total N = 879 couples). In Studies 1 to 3, we found linear but no similarity effects of power on RQ. Experiencing power was positively related to both actor’s and partner’s RQ. In Study 4, again, no similarity but actor and partner effects were found on sexual satisfaction. These findings show that the link between power balance and RQ found in previous research does not hold with sophisticated analysis techniques that overcome issues of previous approaches (e.g., difference scores). In fact, the absolute level of experienced power, not power balance, matters for both RQ and sexual satisfaction. Practitioners may target strengthening an individual’s power instead of focusing on issues of power balance

Staatsräson : Die Suche nach der richtigen Israel-Politik

Dieser Artikel ist Teil 3 eines längeren, dreiteiligen Artikels: Teil 1: https://www.israelnetz.com/das-diktum-von-angela-merkel/ Teil 2: https://www.israelnetz.com/was-beeinflusst-die-deutsche-israel-politik/ Teil 3: https://www.israelnetz.com/die-suche-nach-der-richtigen-israel-politik/Die „deutsche Staatsräson“ gegenüber Israel war von Anfang an auslegbar. Der Gazakrieg und die Luftschläge im Iran verstärkten die Debatte um den Begriff. Welche Prinzipien sollten künftig die deutsche Israelpolitik bestimmen

Rethinking model prototyping through the MedMNIST+ dataset collection

The integration of deep learning based systems in clinical practice is often impeded by challenges rooted in limited and heterogeneous medical datasets. In addition, the field has increasingly prioritized marginal performance gains on a few, narrowly scoped benchmarks over clinical applicability, slowing down meaningful algorithmic progress. This trend often results in excessive fine-tuning of existing methods on selected datasets rather than fostering clinically relevant innovations. In response, this work introduces a comprehensive benchmark for the MedMNIST+ dataset collection, designed to diversify the evaluation landscape across several imaging modalities, anatomical regions, classification tasks and sample sizes. We systematically reassess commonly used Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) architectures across distinct medical datasets, training methodologies, and input resolutions to validate and refine existing assumptions about model effectiveness and development. Our findings suggest that computationally efficient training schemes and modern foundation models offer viable alternatives to costly end-to-end training. Additionally, we observe that higher image resolutions do not consistently improve performance beyond a certain threshold. This highlights the potential benefits of using lower resolutions, particularly in prototyping stages, to reduce computational demands without sacrificing accuracy. Notably, our analysis reaffirms the competitiveness of CNNs compared to ViTs, emphasizing the importance of comprehending the intrinsic capabilities of different architectures. Finally, by establishing a standardized evaluation framework, we aim to enhance transparency, reproducibility, and comparability within the MedMNIST+ dataset collection as well as future research. Code is available at github.com/sdoerrich97/rethinking-model-prototyping-MedMNISTPlus

Cognitive-motor interference in multiple sclerosis revisited : a dual-task paradigm using wearable inertial sensors and the Paced Auditory Serial Addition Test

Introduction: Multiple sclerosis (MS) is a chronic autoimmune disease afecting the central nervous system, leading to motor and cognitive impairment. These impairments become especially evident during dual-tasks, such as walking while performing a cognitive activity. Previous research has highlighted changes in gait-specifc parameters during dual-tasks, but the cognitive component remains underexamined in MS. This study aims to expand on prior fndings by using wearable inertial sensors and the Paced Auditory Serial Addition Test (PASAT) to evaluate the efects of dual-tasks on gait and cognitive performance in persons with MS (PwMS) compared to healthy controls. Methods: Eighty-six adults (54 PwMS and 32 healthy controls) participated. PwMS were further divided into groups with lower (MS_LCP) and higher (MS_HCP) cognitive performance based on performance on the Symbol-Digit-Modalities Test (SDMT). Gait parameters were assessed using wearable inertial sensors during single- and dual-task 3-min-walking. Statistical analyses compared gait and cognitive performance across conditions and groups. Results: Under dual-task conditions, PwMS showed signifcant changes in all gait parameters, including reduced walking speed, stride length, percentage of swing phase and toe clearance, and increased stride time and percentage of stance phase compared to single-task condition. However, under dual-task condition in PwMS only walking speed, stride length and stride time difered from healthy controls. MS_LCP exhibited greater changes in both gait and PASAT performance than MS_HCP and healthy controls. While MS_HCP showed gait parameters comparable to healthy controls during single-tasks, defcits became apparent during dual-tasks. Correlations revealed strong associations between SDMT and PASAT scores but weak links between cognitive and self-reported measures. Discussion: The fndings confrm that dual-task conditions exacerbate gait impairments in PwMS, particularly in those with lower cognitive performance. The use of PASAT as a dual-task cognitive challenge was feasible and had a considerable infuence on gait. Results support the capacity sharing theory, suggesting that limited cognitive resources are redistributed between tasks under dual-task conditions

The group mind of hybrid teams with humans and intelligent agents in knowledge-intense work

Studies regularly demonstrate how well intelligent agents (IAs) can support humans or are demonstrably superior to them in some areas. Given that some tasks likely remain unsuitable for even the most intelligent machines in the mid-future, work in hybrid teams of humans and IAs—where the capabilities of both are effectively combined—will most likely shape the way we work in the coming decades. In an abductive study, we investigate an early example of hybrid teams, consisting of a conversational intelligent agent (IA) and humans, that aims to improve health behavior or change personality traits. We theorize Transactive Intelligent Memory System (TIMS) as a new vision of collaboration between humans and IAs in hybrid teams, based on our empirical insights and our literature review on transactive memory systems theory. Our empirical evidence shows that IAs can develop a form of individual and external memory, and hybrid teams of humans and IAs can realize joint systems of transactive memory—a competence that current literature only ascribes to humans. We further find that whether individuals view IAs merely as external memory aids or as part of their teams’ transactive memory is moderated by the tasks’ complexity and knowledge intensity, as well as the IA’s ability to complete the task. This theorizing helps to better understand the role of IAs in future team-based working processes. Developers of IAs can use TIMS as a tool for requirements formulation to prepare their software agents for collaboration in hybrid teams