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Asymmetric changes in the cooling capacity of China's lakes
No full textLakes significantly influence local climate, yet a systematic assessment of their cooling effect across diverse regions remains limited. This study develops a multi-metric (spatial extent, magnitude, and efficiency) framework to evaluate the spatiotemporal patterns of Lake Cooling Capacity (LCC) for 265 major Chinese lakes from 1980 to 2022. Results show that Chinese lakes exert substantial cooling on summer daytime maximum temperatures, with a mean extent of 27.5 km, a magnitude of 1.03 degrees C, and an efficiency of 0.46 degrees C/10 km. LCC efficiency shows spatially asymmetric trends, intensifying on the Tibetan Plateau but weakening in the eastern plains. Random forests analysis reveals that albedo is the dominant driver of temporal variability, while depth and surrounding topography are the primary spatial controls. These findings underscore the critical role of lakes in mitigating regional heat extremes and highlight the necessity of incorporating lake-climate feedback into climate adaptation
Soil moisture retrieval from Sentinel-1: lessons learned after more than a decade in orbit
No full textSoil moisture is a critical variable for hydrology, agriculture and climate. However, large-scale soil moisture observation remains difficult due to sparse in situ networks and the inability of optical sensors to capture it under cloud cover. Synthetic aperture radar (SAR) missions, e.g., Sentinel-1, yield unique all-weather, day and night observations with a fine spatial and temporal resolution that makes them of interest for development of global soil moisture monitoring. Consequently, this review discusses the application of C-band SAR observations from the Sentinel-1 satellite mission to estimate high-resolution near-surface soil moisture. First, the importance of SAR backscatter monitoring from Sentinel-1 is emphasized. Next, the current state-of-the-art in soil moisture retrieval from Sentinel-1 is presented. Although considerable progress has been made in near-surface soil moisture retrieval, several limitations remain. Factors such as the effects of vegetation and surface roughness on the signal, sensor and scattering model limitations, spatial and temporal constraints, and uncertainties, e.g. in data assimilation, pose challenges to its usage. While Artificial Intelligence (AI)-based retrieval methods have shown promise, their interpretability, dependence on large datasets, vulnerability to data quality, and computational burden have been major challenges. Beyond methods that rely on backscatter, there have been recent works indicating that SAR interferometric observables have the potential to estimate soil moisture, especially in arid and semi-arid regions where these are particularly sensitive to moisture changes. To address these challenges, this paper recommends integrating Sentinel-1 with other satellite mission data for a multi-sensor data integration approach (e.g., Sentinel-2 and Soil Moisture Active Passive - SMAP data), refining physical and semi-empirical models, developing advanced AI techniques able to consider physical principles, and combining with emerging data from other high temporal resolution SAR missions (e.g., NASA-ISRO SAR). The review concludes with identification of key research priorities, including standardization of retrieval frameworks, improved validation efforts on standardized reference sets, and cloud processing for real-time user cases. Overall, the review provides a thorough foundation for understanding, refining, and advancing Sentinel-1 based soil moisture retrieval methods
Bewertung eines Digitalen Zwillings durch eine Quantifizierung des Nutzens
No full textIm Kontext von Industrie 4.0 gewinnen digitale Technologien und Konzepte in Unternehmen immer mehr an Bedeutung, da sie Lösungen für aktuelle Herausforderungen bieten können. Insbesondere der Digitale Zwilling kann in verschiedenen Anwendungsbereichen eingesetzt werden und so einen Mehrwert für Unternehmen schaffen. Jedoch stellen die Vielfalt der unterschiedlichen Anwendungsmöglichkeiten sowie Definitionen und die schwierige Bewertung des Mehrwerts des Digitalen Zwillings Unternehmen vor große Herausforderungen. Um Unternehmen bei diesem Entscheidungsprozess zu unterstützen, wird in dieser Dissertation eine Methode zur Bewertung der unterschiedlichen Einsatzmöglichkeiten des Digitalen Zwillings im Produktlebenszyklus vorgestellt. Der vorgestellte Bewertungsansatz fokussiert sich dabei auf die simulative Quantifizierung des Nutzens, denn für Unternehmen steht vor allem der entstehende Nutzen aus der Einführung eines Digitalen Zwillings im Vordergrund. Mithilfe der simulativen Quantifizierung lässt sich jedoch nur der direkte und indirekte Nutzen quantifizieren. Daher wird zur Bewertung des strategischen Nutzens die Digital Twin Balanced Scorecard eingeführt und angewendet. Vor der Ermittlung des Nutzens zeigt die Methode auf, wie unterschiedliche Einsatzmöglichkeiten des Digitalen Zwillings in den verschiedenen Produktlebenszyklusphasen mithilfe eines Einsatzmöglichkeitenkatalogs identifiziert werden können. Zudem wird ein Analyseschema vorgestellt, mit welchen Anforderungen der identifizierten Einsatzmöglichkeit an den Digitalen Zwilling festgestellt werden können und welche Voraussetzungen die IT-Infrastruktur des Unternehmens zur Erfüllung der ermittelten Anforderungen bereitstellen muss. Auf der Basis dieser Ergebnisse werden die Kosten für die Implementierung und Anwendung des Digitalen Zwillings geschätzt. Abschließend wird der Mehrwert durch die Gegenüberstellung der Lebenszykluskosten vor und nach dem Einsatz des Digitalen Zwillings bestimmt.In the context of Industry 4.0, digital technologies and concepts are becoming increasingly important in companies as they can offer solutions to current challenges. The digital twin can be used in various application areas and thus create added value for companies. However, the variety of different possible applications and definitions and the difficulty of assessing the added value of the digital twin present companies with major challenges. To support companies in this decision-making process, this dissertation presents a method for evaluating the various possible applications of the digital twin in the product life cycle. The evaluation approach presented focusses on the simulative quantification of the benefits, as companies are primarily interested in the benefits resulting from the introduction of a digital twin. However, only the direct and indirect benefits can be quantified using simulative quantification. The Digital Twin Balanced Scorecard is therefore introduced and applied to evaluate the strategic benefits. Before determining the benefits, the method shows how different possible uses of the digital twin can be identified in the various product life cycle phases with the help of a catalogue of possible uses. In addition, an analysis scheme is presented which can be used to determine the requirements of the identified application options for the digital twin and which prerequisites the company's IT infrastructure must provide to fulfil the identified requirements. Based on these results, the costs of implementing and using the digital twin are estimated. Finally, the added value is determined by comparing the life cycle costs before and after the use of the digital twin
Predictive maintenance for optical rare-earth doped fiber amplifiers
No full textCommunication has become an integral part of today's society. There are countless services that rely on the special form of telecommunication. Long-haul optical transmission networks in particular represent the backbone of this kind of communication. The core components of optical transmission links are optical rare-earth doped fiber amplifiers, which amplify signals after a certain distance and thus enable transmission distances of several thousand kilometers. The optical amplification mechanism is made possible by pump lasers, which is why various techniques have been developed to monitor this component. Degradation effects of pump lasers in particular lead to problems when reconfiguring optical transmission links. Due to the dynamic environments in which the optical rare-earth doped fiber amplifiers are embedded, there are a variety of operating conditions. This represents a challenge for existing monitoring procedures. With the rapid advancement of technology, new approaches have been developed in industrial processes and modern machines. Predictive maintenance, is an essential component of the internet of things. Predictive maintenance extends conventional monitoring and presents itself as a framework consisting of three main components: anomaly detection, prognosis and diagnosis. By using novel data-driven architectures, modeling of complex multi-component systems can be achieved, whereas the application of monitoring and more advanced techniques onto pump lasers can be extended to all critical components of an optical rare-earth doped fiber amplifier. Therefore, predictive maintenance is applied to the optical rare-earth doped fiber amplifiers, taking into account the main components mentioned. In the context of anomaly detection, a novel change-detection framework is proposed, which robustly localizes trends in multiple operating conditions. Subsequently, the remaining useful lifetime is estimated in a temporal context using a prognosis component. A diagnosis component is used to classify the fault case that leads to the degradation of the optical rare-earth doped fiber amplifier. The prognosis and diagnosis component use complex transformer-based deep learning models, while the anomaly detection uses shallow machine learning algorithms. In combination, a predictive maintenance framework is generated. By extracting information about an abnormal operating state, the remaining useful lifetime and the underlying fault case of an optical rare-earth doped fiber amplifier, maintenance measures can be planned and carried out. This results in an increased availability and reliability of optical transmission links
Il dialetto nel paesaggio linguistico napoletano: tra localismo e globalizzazione
No full textIl contributo si inquadra nell’interesse per l’“usabilità del dialetto” (Berruto 2012) nell’Italia contemporanea, concentrandosi sullo studio del dialetto non come varietà parlata né in quanto dialetto artistico o letterario, ma come risorsa – scritta – per costruire il paesaggio linguistico dello spazio urbano. Sulla base di un ampio campione di fotografie delle insegne commerciali di esercizi attivi nel campo della ristorazione esposte in diversi quartieri della città di Napoli, ci si propone di classificare gli usi e le funzioni del dialetto nella scrittura pubblica per analizzare il ruolo e la presenza del napoletano nella costruzione linguistica dello spazio cittadino. Lo studio, condotto sulla base di un approccio di tipo qualitativo, presenta una selezione di scritture esposte interamente in dialetto o plurilingui valendosi degli strumenti della linguistica del contatto e in particolare del modello di analisi di testi scritti multimodali multilingui sviluppato da Mark Sebba (2012, 2013)
Impact of ligand-mediated inductive effects on electrochemical p-doping of CsPbBr3 nanocrystals
No full textLead halide perovskite nanocrystals (NCs) are promising materials for light-emitting diodes (LEDs) due to their wavelength tunability, narrow emission line width, and high photoluminescence quantum yield. Oftentimes, these devices suffer from charge carrier imbalance and reduced charge injection because as-synthesized NCs are covered by long aliphatic ligands. Here, we report ligand exchange to small electron-withdrawing or -donating cinnamate ligands. We probe the influences of the ligands’ inductive effect on hole injection by photoluminescence spectroelectrochemistry (PL SEC). We find that hole injection into NCs covered by electron-withdrawing ligands is facilitated, and hole-only devices exhibit higher currents compared to electron donating ligands. Our work highlights the potential of PL SEC as a powerful tool to rationalize the performance of lead halide perovskite NCs in LEDs
Relations between university teachers' teaching‐related coping strategies and well‐being over time: a cross‐lagged panel analysis
Get PDFBackground
University teachers' well-being plays a critical role in their productivity and educational effectiveness. Apart from cross-sectional research on demographic and institutional/contextual correlates, insight into potential causes and consequences of faculty well-being is limited. This includes insight into relations between different coping strategies and well-being.
Aims
We studied the interplay of different strategies for coping with teaching-related stress with university teachers' well-being over the course of one semester.
Sample
Participants were 489 German university teachers (age: M = 41.1 years, SD = 11.4) from 34 universities. Their demographics were characteristic of German university staff.
Methods
Participants reported on their use of task-oriented, emotion-oriented and avoidance-oriented coping to manage teaching-related stress and on their subjective well-being (positive and negative affect; job satisfaction) at the beginning (T1: November) and end (T2: February) of the winter 2020/2021 term. Interrelations were examined via cross-lagged panel analysis.
Results
Task-oriented coping was positively related to the slope of changes in positive affect, and vice versa, over time. Emotion-oriented coping (rumination) was positively related to the slope of changes in negative affect, and negatively related to the slope of changes in positive affect and job satisfaction. Negative affect was positively related to the slope of changes in avoidance-oriented coping.
Conclusions
The findings provide directions for further developing supportive measures for promoting well-being in university teaching staff by highlighting the relevance of different coping strategies as causes and consequences thereof. Task-oriented coping may be particularly adaptive for well-being: at the same time, interventions aiming to promote well-being may also facilitate task-oriented coping behaviours
In the beginning was the word: LLM-VaR and LLM-ES
Get PDFThis study introduces LLM-VaR and LLM-ES, novel risk estimation metrics that utilize general-purpose large language models (LLMs) for the forecasting tasks of Value at Risk (VaR) and Expected Shortfall (ES) in a zero-shot setting. Building on the input encoding mechanism of the LLMTime framework, we extend its application by defining new financial risk measures and performing an empirical evaluation of three generations of GPT models, GPT-3.5, GPT-4 and GPT-4o, versus advanced benchmark models such as GARCH with Student innovations and EWMA with Dynamic Conditional Score (DCS).
Financial time series are encoded as numerical strings, allowing for model-free inference without requiring retraining. Results show that LLMs perform well when short rolling windows are used, particularly in volatile markets like cryptocurrencies. GPT-3.5 frequently outperforms or matches the performance of newer models, raising questions about model complexity, alignment, and biases. In contrast, performance deteriorates with longer windows, where the econometric models prove more reliable. Our findings demonstrate the potential of general-purpose LLMs as adaptive tools for short-horizon financial risk assessment and contribute a first-of-its-kind benchmark for LLM-based VaR/ES estimation