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In this paper, we augment a model of endogenous credit cycles by Matsuyama et al. (2016) with human capital to study its impact on the stability of income evolution and its distribution. Human capital is modelled as pure external effect of production, following a learning-by-producing approach. Agents have access to two different investment projects, which differ substantially in their next generations spillover effects. Some generate pecuniary externalities and technological spillovers through human capital formation whereas others fail to do so. Moreover, the latter are subject to financial frictions. Together with the interaction between those projects, endogenous credit cycles occur and a pattern of boom and bust cycles can be observed. We explore the impact of human capital on the systems stability by providing analytical results and numerical simulations, which confirm a strong interaction between credit market frictions and the importance of human capital in the production process. In general, we found that an increasing importance of human capital has an ambiguous effect on the income evolution. Especially during transition periods, we observe a destabilising effect while with high human capital importance, the income path is stabilised. Moreover, an increase of the importance of human capital favours investment projects with positive intergenerational spillovers, thus driving up the labour income of the young generation. Thus, our study reveals that human capital is an essential factor for economic stability under the presence of financial frictions and has substantial positive effects on the income distribution.

The aim of this study was to develop the profile and competencies of a harmonised curriculum for a multi-country regional sector-specific higher education programme. The study, which was based on a case study of Renewable Energy involving six countries in Southern Africa, was based on established methodologies proposed by previous studies on harmonisation and profiling of higher education programmes. The study uses a general curriculum development approach leading to the definition of generic and specific competencies and feeding into a learning taxonomy to create alignment with specific learning outcomes at the intended qualification level. However, the approach extends the harmonisation concept to include industrialisation potential, which is crucial in the developing-country context. This allows for the exploitation of shared resources in sector-specific technology areas and supports the development of regional standards on technology and practice, as part of developing a sustainable regional economic sector. Twentytwo competencies, encompassing both generic and specific competencies, were defined. These competencies were grouped into six key competence areas, and later transformed into four clusters of competencies: core competencies, hard skills, soft skills and attitudes. These four clusters were then placed on a planetary system to represent the meta-profile for the qualification, which forms the framework for the future design of learning materials for the qualification. Received: 24 June 2019Accepted: 5 October 2020

The potential of combining biogas and photovoltaic (PV) power plants in hybrid systems in the German distribution grid is analyzed. The focus of the present research is on balancing the intermittent power supply from PV power plants with the controllable power production of combined heat and power (CHP) units of biogas plants within a period of seconds. To achieve an increase of the total energy feed-in of biogas and PV power plants to the electricity grid, a biogas plant energy management system is described. System parameters, such as the variable feed-in of PV power plants or power ramps of the start-up process of controllable biogas plant CHP unit, are described and adapted to the global installed capacity of PV power plant and biogas plants in the German distribution grid.

Achieving high productivity growth is a central goal of policymaking, given that productivity (growth) impacts not only key macroeconomic variables, but also a country's living standards. Central banks have an intrinsic interest in promoting productivity growth because of its interaction with the natural rate of interest r*, a key variable of monetary policy. The natural rate of interest is also crucial for the scope of monetary policy space - both in terms of conventional and unconventional policies. Summarizing recent empirical evidence, we present nine hypotheses about why productivity dynamics may have slowed down recently in industrial countries around the world: (1) lack of (investment) demand; (2) expansionary monetary policy; (3) firm size and age; (4) technological cycles, the nature of recent innovations and the time it takes to apply them productively; (5) weak technological diffusion; (6) subdued creative destruction; (7) financial market dynamics and valuation; (8) population aging; and (9) regulation and the compliance burden. The factors that will shape future productivity trends may differ from today's and past drivers. For this reason, we highlight three policy fields that may become even more critical over the next decades: population aging, digitalization and climate change. We conclude that weak productivity growth must be approached from various angles. Appropriate policy mixes may differ widely across global and European regions. Here, central banks can play a crucial role in helping governments find this appropriate policy mix.

Background: Walking impairment is a common and highly disabling symptom in people with MS (PwMS). Ambulatory deterioration is poorly characterized in PwMS and reliable prognosis that may guide clinical decisions is elusive. This study aimed to objectively track the progression of clinical walking performance and kinematic gait patterns in PwMS over 4 years, thereby revealing potential prognostic markers for deterioration of ambulatory function. Methods: Twenty-two PwMS (48.8 ± 9.9 years, 14 females; expanded disability status scale [EDSS]: 4.5 ± 0.9 points) with gait impairments were recruited at the University Hospital Zurich, Switzerland. Gait function was monitored over a period of 4 years using a set of standardized clinical walking tests (timed 25-foot walk [T25FW], 6 min walk test [6MWT], 12-item MS walking scale [MSWS-12]) and comprehensive 3D kinematic gait analysis. Walking decline was assessed in the full patient cohort and in patient sub-groups that were built according to MS type (relapsing-remitting [RRMS], progressive [PMS]) and subjects' pathological gait signature (cluster groups 1–3). Results: In the total cohort (n = 22), we found a significant worsening in the 6MWT (BL vs. 4y: -41.1 m; P = 0.0053), while the performance in the T25FW, MSWS-12 and the EDSS remained unchanged over 4 years. Subjects with PMS (n = 12) showed a significant worsening in the EDSS (BL vs. 4y: +0.6 points; P = 0.0053), which was not observed in participants with RRMS (n = 10). Whereas deterioration of clinical walking function was not different between subjects with RRMS and PMS, we identified differences in clinical walking deterioration between PwMS with varying gait pattern pathologies: Subjects with spastic-paretic gait impairments (cluster 1; n = 9) demonstrated a marked worsening in the T25FW (BL vs. 4y: +2 s; P = 0.0020) and 6MWT (BL vs. 4y: -92.9 m; P < 0.0001) which was not seen in PwMS with an ataxia-like (cluster 2; n = 8) or unstable walking pattern (cluster 3; n = 5). Deterioration of clinical walking performance in cluster 1 was accompanied by a specific worsening of gait deficits that were characteristic of this cluster at baseline, a phenomenon not found in the other sub-groups. Accordingly, aggravation of cluster 1-specific gait impairments over 4 years predicted deterioration of the 6MWT in the total cohort (n = 22) with an accuracy of 90.9% (sensitivity: 90.9%; specificity: 90.9%; Nagelkerkes coefficient of determination R2: 0.721), unveiling key determinants of MS-related walking decline. Conclusions: Our findings highlight the potential of quantitative, functional outcomes for objective tracking of disease progression in PwMS. Gait pattern analysis can provide valuable information on the underlying pathomechanisms of gait deterioration and may represent a complementary prognostic tool for walking function in PwMS. Clinical trial: clinicaltrials.gov, NCT01576354, Multiple Sclerosis and Related Disorders, 63

Die integrierte Vermittlung von sprachlichen und fachlichen Inhalten im Unterricht wird mittlerweile sowohl in der universitären Lehrer_innenbildung als auch in der Schule verstärkt thematisiert. Ein zentrales Thema dabei ist insbesondere auch die sprachsensible Gestaltung von Aufgabenstellungen. Basierend auf dem Konkretisierungsraster von Tajmel und Hägi-Mead (Tajmel, 2011; Tajmel & Hägi-Mead, 2017) wurde ein Instrument für den Geschichtsunterricht entwickelt, mit dem Aufgabenstellungen sowohl sprachsensibel aufbereitet als auch die spezifischen fachlichen Anforderungen dezidiert herausgearbeitet werden können. Das Instrument, welches im vorliegenden Beitrag vorgestellt wird, eignet sich für den Einsatz in der sprachsensiblen Unterrichtsvorberei-tung bzw. für die Aus- und Weiterbildung von (angehenden) Lehrkräften., Language-based strategies in subject matter teaching to foster L2 learners is increasingly being addressed both in university teacher education and in schools. A central topic here is the language-based work with tasks. Based on the “Konkretisierungsraster” of Tajmel (Tajmel, 2011; Tajmel & Hägi-Mead, 2017) an instrument for history teaching has been developed, which allows working with tasks in such a way that language facilitation within subject matter teaching is pos-sible. The instrument is suitable for the use of language-based strategies in subject matter teaching as well as for the training and further education of (pre-service) teachers.

The floodplain areas along the Naryn River in Kyrgyzstan are essential as they hold the riparian (local) ecosystems because they provide firewood, pastureland, and areas for recreation and protection against erosion. Due to limited access to adequate, reliable, and sustainable energy services, the rural population usually derived their energy needs from multiple natural energy resources (i.e., firewood, charcoal, agricultural residues, animal dung, and wood branches). This is considered a common and predominant practice in rural Kyrgyzstan. This situation leads to a negative impact on local ecosystem services. In addition to that, the reliance on solid fuels contributes to indoor and outdoor air pollution, which is partially threatening the local ecosystem services. By contrast, the employment of renewable-based energy supply systems would substantially reduce the burden on the environment, which is mainly untapped. To integrate renewable energies, it is important to understand the energy behavior of floodplain communities. In that response, the presented article is the first attempt to capture the energy identity of the floodplain community of Kyrgyzstan based on a quantitative energy-based (on-site) household survey. Furthermore, the present research article synthesizes the driving factors that have a (direct or indirect) impact on the energy and local ecosystem services. In addition, the present article proposes a brief pathway for the sustainable energy transition. The article records the recommendation to integrate renewable energies to preserve the local ecosystems of Kyrgyzstan.

In both rural and urban areas, two-wheeler vehicles are the most common means of transportation, contributing to local air pollution and greenhouse gas emissions (GHG). Transitioning to electric two-wheeler vehicles can help reduce GHG emissions while also increasing the socioeconomic status of people in rural Kenya. Renewable energy systems can play a significant role in charging electric two-wheeled vehicles, resulting in lower carbon emissions and increased renewable energy penetration in rural Kenya. As a result, using the Conventional and Renewable Energy Optimization (CARNOT) Toolbox in the MATLAB/Simulink environment, this paper focuses on integrating and modeling electric two-wheeled vehicles (e-bikes) into an off-grid photovoltaic Water-Energy Hub located in the Lake Victoria Region of Western Kenya. Electricity demand data obtained from the Water-Energy Hub was investigated and analyzed. Potential solar energy surplus was identified and the surplus was used to incorporate the electric two-wheeler vehicles. The energy consumption of the electric two-wheeler vehicles was also measured in the field based on the rider’s driving behavior. The modeling results revealed an annual power consumption of 27,267 kWh, a photovoltaic (PV) electricity production of 37,785 kWh, and an electricity deficit of 370 kWh. The annual results show that PV generation exceeds power consumption, implying that there should be no electricity deficit. The results, however, do not represent the results in hourly resolution, ignoring the impact of weather fluctuation on PV production. As a result, in order to comprehend the electricity deficit, hourly resolution results are shown. A load optimization method was designed to efficiently integrate the electric 2-wheeler vehicle into the Water-Energy Hub in order to alleviate the electricity deficit. The yearly electricity deficit was decreased to 1 kWh and the annual electricity consumption was raised by 11% (i.e., 30,767 kWh), which is enough to charge four more electric two-wheeler batteries daily using the load optimization technique.